Senior Machine Learning Engineer at Twelve Labs | Seoul

Who We AreJoin us in setting global standards for video understanding AI! Twelve Labs is dedicated to developing cutting-edge AI models specifically for video content, enabling efficient processing of vast amounts of video data. Our technology offers advanced capabilities for search, analysis, summarization, and generating insights from video.Our models are …

Welcome to the Journey of Joining the Daangn Team!At Daangn, we are committed to creating an environment where individuals can grow alongside the company's success.The Daangn recruitment team is here to ensure that you can engage in meaningful discussions with wonderful colleagues. Introducing the Software Engineer, Machine Learning RoleThe Software Engineer specializing in Machine Learning at Daangn leverages machine learning to enhance various services, ultimately improving user satisfaction and fostering connections within local communities. This role involves recommending personalized and engaging content for users on home feeds and detail pages, as well as facilitating efficient advertising to help local residents promote their offerings.Key Responsibilities[Feed Quality Team]Develop and enhance ML models to boost user engagement and foster diverse and serendipitous discoveries on the feed.Analyze data to formulate hypotheses, identify improvements based on theoretical foundations, and validate through online experiments to achieve tangible service impact.Develop and refine models for a deeper understanding of users and content.Utilize LLM in various ways to enhance personalized recommendations.[Advertising Recommendation Team]Develop and improve CTR and CVR prediction models, as well as automated bidding, targeting, and pacing models for ad recommendations and rankings.Analyze user behavior data and advertising performance, formulating data-driven hypotheses and validating them through A/B tests to enhance both advertising efficiency and user experience.Recommend meaningful ads to users through candidate ad extraction and ranking models that consider personalization.Enhance advertising targeting accuracy using LLM and the latest AI technologies, deepening the understanding of users and ad content.Who We're Looking ForIndividuals with a solid foundation in machine learning theory and principles.A deep understanding of deep learning (Recommendation, NLP, Graph Neural Networks, Reinforcement Learning, Vision, or more).Experience in conceptualizing efficient code architecture and writing readable code.Preferred QualificationsExperience making a tangible impact on user services through machine learning in a data-driven environment.A keen interest in the latest machine learning and deep learning trends, enjoying reading relevant papers.Experience with ML-based recommendation and advertising systems.Experience deploying machine learning models and systems in large-scale production environments.Familiarity with BigQuery, Cloud Dataflow, Kubeflow, TFX, and TF Serving.Additional InformationThis full-time position includes a 3-month probation period.In accordance with the 'Act on the Promotion of Employment for Persons with Disabilities' and the 'Act on the Honorable Treatment and Support of Veterans', individuals with disabilities and veterans are given preference during the recruitment process.Video interviews will include live coding tests and a brief assessment of ML fundamentals. Please prepare your PC environment for a smooth testing experience.

Welcome to the Journey of Joining the Daangn Team!At Daangn, we are committed to fostering an environment where individuals can grow alongside the company's success.Our recruitment team is here to assist you in achieving those joyful moments of collaboration with amazing colleagues. Introducing the ML Infrastructure TeamThe ML Infrastructure Team within our Infrastructure Department is responsible for developing a robust and scalable machine learning infrastructure that ensures effective service delivery and efficient operation of Daangn’s machine learning-based services. Machine learning is extensively utilized at Daangn to enhance service quality and improve user convenience across various domains, including feed recommendations, ad recommendations, and service operations. The ML Infrastructure Team handles everything from data processing, model training, model serving, to the deployment processes necessary for machine learning service development. Daangn's GenAI Platform Building Serverless ML Training Infrastructure: Vertex AI Pipelines & TFX ML Infrastructure with GCP | 2025 Daangn GCP MeetupYour ResponsibilitiesDevelop and manage model servers and serving systems for efficient deployment of various machine learning models.Develop and maintain ML infrastructure SDKs, frameworks, and training systems used across the organization.Create specialized monitoring systems for machine learning services to detect quality changes early.Implement various optimization methods across the machine learning infrastructure to enhance development iteration speed and resource efficiency.We Are Looking ForA proficient user of one or more programming languages such as Python or C++.Strong understanding of the infrastructure required for training and serving machine learning models.Over 7 years of experience in backend service or machine learning service development/operations.A desire to improve machine learning infrastructure through solid software engineering skills.Experience in developing and operating GPU clusters.Preferred QualificationsFamiliarity with cloud services like AWS and GCP, with practical experience.A deep understanding of the machine learning ecosystem and contributions to open source projects (e.g., TensorFlow, PyTorch, TensorFlow Extended).A keen interest in new technological trends and a willingness to learn.Additional InformationFor full-time hires, there is a 3-month probation period.We prioritize individuals with disabilities and veterans according to the Employment Promotion and Vocational Rehabilitation Act and the Act on the Honorable Treatment and Support of Veterans.Application Process1. Document Screening → 2. Video Interview → 3. Technical Interview → 4. Culture Fit Interview and Reference Check → 5. Compensation Negotiation → 6. Final Acceptance and OnboardingGo to Daangn Joining Journey Guide

Who We AreAt Twelve Labs, we are on a mission to redefine the global standards for video understanding AI! We are creating world-class AI models tailored for video data, providing specialized search, analysis, summarization, and insight generation capabilities.Our models are utilized by the largest sports leagues worldwide to swiftly and accurately highlight key moments in vast game footage, delivering an ultra-personalized viewing experience. In South Korea, our technology assists integrated command centers in efficiently navigating CCTV footage to respond to crises, while major global broadcasters and studios leverage our models for content creation aimed at billions of viewers.As a Deep Tech startup with offices in San Francisco and Seoul, Twelve Labs has been recognized as one of the world's top 100 AI startups by CB Insights for four consecutive years. We have secured over $110 million in investments from leading VCs and companies including NVIDIA, NEA, Index Ventures, Databricks, and Snowflake. Our AI model, uniquely developed in Korea, is exclusively offered through Amazon Bedrock. We thrive on building innovative products alongside exceptional colleagues and growing with clients worldwide.Our core values are foundational to our work:Honesty and reflection about ourselves and our teamsPerseverance and humility in the face of failure and feedbackA commitment to continuously elevate team capabilities through ongoing learningIf you enjoy tackling challenging problems and growing through the process, the opportunity awaits you at Twelve Labs!About the TeamThe ML Data Team at Twelve Labs believes that 'data determines the performance of AI models.' We construct high-quality data end-to-end necessary for training and evaluating multimodal AI models. Our team is responsible for collecting, filtering, processing, and labeling diverse multimodal data such as video, image, and audio. We collaborate with various teams to design training data that can unleash new model capabilities, while also developing evaluation datasets that reflect real user experiences. To perform all these processes efficiently, we develop and continuously improve internal tools.Through a meticulously designed data pipeline, the ML Data Team plays a crucial role in developing Twelve Labs' world-class video understanding models.About the RoleAs the Engineering Manager for the ML Data Team, you will be a pivotal leader in building and guiding a high-performing team, while also developing the large-scale data infrastructure necessary for AI model training. This position encompasses team operations, technical architecture design, engineering recruitment and mentoring, and enhancing project execution, all aimed at fostering a high-quality data ecosystem that supports Twelve Labs' products and research.In this Role, You WillTeam Building and Technical Culture Development Lead the ML data engineering team by defining and executing the team's mission and technical strategy. Establish and execute recruitment plans to attract top talent, while fostering a high-level engineering culture through code reviews and technical mentoring.Design and Operate Multimodal ML Data Engine Design and operate pipelines for collecting, refining, and labeling petabyte-scale video, image, and audio data for AI model training. Additionally, build an automated dataset generation system for VLM/LLM training and a high-precision preprocessing engine.Design and Operate Multimodal ML Data Infrastructure Create and manage scalable data infrastructure capable of reliably processing large-scale data.Establish and Execute Data Strategy Collaborate closely with PM, research, infrastructure, product, and other organizations to establish and prioritize a data strategy aligned with the company's vision.

Welcome to Your Journey with daangn!At daangn, we strive to foster an environment where individual growth aligns with company development.The daangn recruitment team is here to assist you in experiencing the joy of engaging with wonderful colleagues! Introducing the Search Quality TeamThe Search Quality Team focuses on revolutionizing the search experience at daangn by utilizing machine learning and natural language processing technologies to deliver personalized results. Our mission is to help users find the information they desire quickly and accurately through a contextual understanding of neighborhood life. The team is divided into three parts: Search Vertical, Search Fleamarket, and Search Place, with each ML engineer specializing in models tailored to various domains such as second-hand trading, local living, and maps.Your RoleUnderstand user search intent to recommend personalized keywords, collections, and products.Enhance search quality using natural language processing, graph-based models, and personalization algorithms.Design lightweight architectures for real-time model serving.Manage experiments and operations for model improvements in collection ranking, knowledge graphs, and keyword suggestions.We Are Looking ForExperience in designing and operating machine learning-based recommendation/search/ranking systems.Background in developing natural language processing (NLP) or graph-based recommendation systems.Experience building Python-based machine learning training pipelines.Experience in experimental design and A/B testing using large datasets.Experience in system implementation considering model serving and performance optimization.Bonus Points If You HaveModeling experience with search query auto-completion, related search terms, and typo correction.Experience serving real-time deep learning model inference.Experience leading specific domains (ranking/recommendation) with ownership in an ML engineering team.Please NoteOnboarding content and responsibilities may vary according to your capabilities.Full-time hires will undergo a 3-month probation period.According to the 'Act on the Promotion of Employment for the Disabled' and the 'Act on the Honorable Treatment and Support of Veterans', candidates with disabilities and veterans will receive preferential treatment during the recruitment process.The Application Process1. Document Screening → 2. Video Interview → 3. Job Interview → 4. Culture Fit Interview → 5. Final Acceptance → 6. JoiningGo to daangn's Joining Journey Guide ()

# About the Team- As an MLE (Commerce Recommendation) in the Toss Commerce domain, you will play a pivotal role in optimizing product visibility.- Our focus is on designing and enhancing recommendation models based on diverse data sources to present users with more relevant and appealing products.- This team leads the full spectrum of machine learning development, from problem definition to training, performance analysis, and enhancement.- We are building a product recommendation platform that provides meaningful shopping experiences for users by leveraging various ML techniques.- **Interested in learning more about Toss's Data Organization?** [→ *Toss Data Division Wiki*](https://recruit-data-division.oopy.io/)# Responsibilities- Develop models to predict product click-through rates (CTR), conversion rates (CVR), and other key metrics based on user behavior, product information, and contextual data.- Design and refine recommendation algorithms to optimize product exposure using predictive outcomes.- Conduct iterative experiments including model performance analysis, feature engineering, and hyperparameter tuning to enhance recommendation quality.- Quantitatively validate the impact of model improvements through various experiments and offline/online performance metrics.- Collaborate with domain experts and data analysts when necessary to accurately define and solve recommendation challenges.# Ideal Candidate- We prefer candidates with experience in developing recommendation systems or predictive ranking models.- Candidates who have designed and improved machine learning models for predicting user responses, like CTR and CVR, will be highly regarded.- Experience in experimenting, tuning, and analyzing models using various features is a plus.- Familiarity with major ML frameworks such as PyTorch, TensorFlow, and LightGBM is advantageous.- Candidates who have engaged deeply in problem definition and performance analysis beyond merely training models are welcome.- Strong communication skills and the ability to think data-driven while articulating complex problems clearly are essential.# Resume Tips- Detail any impactful projects you have undertaken that influenced your organization significantly.- Explicitly describe the problems you defined, the approaches you selected, and the methods you used in modeling-centered projects.- Highlight any quantitative problem-solving processes you have engaged in through iterative experimentation and performance analysis.# Journey to Join TossApplication > 1st Technical Interview (Coding) > 2nd Technical Interview > Cultural Fit Interview > Reference Check > Offer Negotiation > Final Acceptance- The first technical interview will include a simple coding test, resume review, and ML fundamentals assessment.- The second technical interview will focus on in-depth technical discussions and ML system design.# A Note for Potential Colleagues> "This role goes beyond mere modeling; it makes an impact in business."- The most fulfilling aspect of working at Toss is that we do much more than just modeling.- Previously, my tasks were limited to inputting data into existing models for performance evaluation, but now I am engaged in integrating unaggregated data into models and contributing to impactful solutions based on user understanding for our super app!

Neural Concept creates AI-driven tools for engineering and design teams, with a focus on 3D deep learning and engineering intelligence. The company’s platform supports clients in automotive, aerospace, and energy as they improve product design processes. Based in Seoul, Neural Concept works with industry leaders to solve engineering challenges and speed up innovation. Role overview The Machine Learning Application Engineer role bridges engineering and artificial intelligence. This position is part of the ML Application Engineering team and involves direct collaboration with clients throughout Korea. The focus is on understanding each client’s technical needs and delivering practical, tailored solutions. Daily work includes close interaction with engineering and design teams as they adopt AI and machine learning in their product development cycles. What you will do Partner with customer engineering teams to manage projects, analyze and process engineering data using the Neural Concept platform and Python libraries, and develop custom workflows. Design and implement machine learning and deep learning solutions for engineering and physics challenges, including building proofs-of-concept for CAD, CAE, and manufacturing applications. Train client teams on Neural Concept’s platform and methods, ensuring smooth integration of AI into their workflows. Work with internal developers by sharing customer feedback and helping improve the product. Requirements Strong drive to address real-world engineering problems using machine learning and automation. Master’s or PhD in Engineering, Applied Mathematics, or Physics. Solid grasp of engineering fundamentals and machine learning techniques. Location Seoul, Korea

Join Our Team As a Machine Learning Engineer at Toss Securities, you will be part of the AI Tribe, collaborating with Data Engineers, Server Engineers, Frontend Engineers, Product Owners, and Product Designers. The AI Tribe aims to create data services that provide essential information to investors using data from various securities domains and cutting-edge Machine Learning technologies. Our focus is on developing personalized recommendation systems and utilizing LLM-based technologies. Your Responsibilities Develop personalized services based on diverse securities domain data and user behavior data. Experiment with reinforcement learning, deep learning, and machine learning methodologies to build recommendation models. Formulate and validate hypotheses regarding user information consumption, enhancing our services in the process. Define the criteria for recommendations and user profiles beyond simple item suggestions. Ideal Candidate Experience in validating hypotheses through personalization and recommendation systems in real-world applications. Strong foundational knowledge of recommendation system domains. Hands-on experience experimenting with and optimizing various deep learning and machine learning-based recommendation models. Familiarity with modeling based on actual user behavior data from apps. Resume Tips Detail your projects or services, including their objectives and outcomes. Focus on impactful projects or services. Explain the problems you solved and the technologies you used. If certain information is sensitive, please omit those details. Technologies Used at Toss Securities Predict user behaviors through User Modeling based on diverse user data. Build deep learning recommendation models by defining and utilizing User Features. Implement Multi-Armed Bandit (MAB) technology in our recommendation services. Generate and validate data as needed, fine-tuning and testing Large Language Models. Conduct experiments and build Retrieval/Chunking/Reranker/Generation models to establish RAG systems. Application Process Application Submission > Job Interview > Cultural Fit Interview > Reference Check > Compensation Negotiation > Final Offer and Onboarding Important Notes Any false information found in your resume or documents may lead to cancellation of your application. Individuals prohibited from hiring under Toss Securities regulations may have their applications canceled. Disabled individuals and those eligible for national veterans' benefits will be given preference in accordance with relevant laws. A Message to Future Colleagues “We are looking for colleagues who are ready to innovate financial services through Machine Learning technology!” Investors in the financial market require vast amounts of information to make informed decisions, but knowing what information to seek and where to find it can be challenging.

# Join Our Team- As a Machine Learning Engineer (MLE) for Home Recommendations, you will play a crucial role in optimizing the recommendation strategies for various content, services, promotions, and messages within the Toss app using machine learning.- Your work will involve precisely modeling the effectiveness of different content at various user touchpoints such as the top of the Toss home screen, banners after money transfers, and push notifications, while continuously improving actual recommendation outcomes.- To enhance the quality of recommendations across diverse areas, you will focus on quantitative performance forecasting and the design of sophisticated exposure strategies.# Responsibilities- Design and enhance recommendation models for content, services, and promotions displayed across the Toss app.- Develop models to predict appropriate recommendations and responses (e.g., CTR) based on user behavior, timing, and context.- Engage in iterative improvement of recommendation systems through feature discovery, experimental design, and performance evaluation.- Optimize exposure strategies for various recommendation areas and push messages based on user responses, quantitatively measuring actual performance.# Who We Are Looking For- Individuals with practical experience dealing with recommendation systems, ranking, and personalization modeling are essential.- A background in designing and experimenting with CTR prediction, user response prediction, and ranking modeling is crucial.- Experience in feature engineering and model performance enhancement using user behavior data is required.- Proficiency in major ML frameworks such as PyTorch, TensorFlow, and LightGBM is necessary.- Candidates who have improved actual service performance through iterative experimentation and quantitative analysis will be preferred.- The ability to clearly define problems and explain them technically while collaborating with diverse teams is highly valued.# Resume Tips- Please detail any projects you have worked on that have had significant organizational impact.- Specify the problems you defined, the approaches you chose, the experimental methods, and how you improved performance in modeling-centric projects.- Highlight any processes where you quantitatively solved problems through iterative experiments and performance analysis.# Application Process- Application Submission > 1st Technical Interview (Coding) > 2nd Technical Interview > Cultural Fit Interview > Reference Check > Compensation Negotiation > Final Acceptance- The first technical interview will include a simple coding test, resume check, and a basic ML knowledge assessment.- The second technical interview will involve in-depth technical questions and discussions on ML system design.# A Message for Future Colleagues- "This role is not just about modeling; it's about making an impact on the business."- The most satisfying aspect of working at Toss is that we do more than just modeling.- Previously, the work was limited to inputting data into existing models and evaluating performance, but now we focus on how to incorporate unaggregated data into our models.- It is rewarding to contribute to running a super app based on a deep understanding of users, moving beyond just analyzing and modeling financial-related data!

About UsJoin us in setting a global standard for AI in video understanding!At Twelve Labs, we are developing state-of-the-art AI models specialized in video processing, enabling advanced search, analysis, summarization, and insight generation from vast amounts of video data.Our models are utilized by the world's largest sports leagues to swiftly and accurately highlight content from extensive match footage, delivering an ultra-personalized viewing experience. Additionally, integrated control centers in South Korea leverage our technology for efficient CCTV video exploration to respond to crises swiftly. Major broadcasters and studios worldwide harness our models to create content for billions of viewers.Twelve Labs, a deep tech startup with offices in San Francisco and Seoul, has been recognized as one of the world's top 100 AI startups by CB Insights for four consecutive years. We have secured over $110 million in investments from leading VCs and corporations such as NVIDIA, NEA, Index Ventures, Databricks, and Snowflake. Our AI model, uniquely developed in Korea, is available through Amazon Bedrock. We thrive on collaboration with exceptional peers to create innovative products and grow alongside our global clientele.Our core values include:Honesty and reflection regarding ourselves and our team.A spirit of resilience and humility that doesn’t fear failure or feedback.A commitment to continuous learning to enhance team capabilities together.If you enjoy solving challenging problems and growing through collaboration, your opportunity awaits at Twelve Labs.About the TeamOur team focuses on multimodal representation learning and production serving. We integrate various modalities like video, audio, and text into a unified embedding space, ensuring stable serving to thousands of global customers.We conduct experiments on multimodal embedding models within a large-scale distributed learning environment, taking responsibility for the end-to-end process of transforming research findings into real-time inference systems. Leveraging top-tier GPU resources such as the NVIDIA B300, we minimize the transition cycle from research to production.In our rapid development cycles, we collaborate closely with Research, Product, and Infrastructure teams to create significant technical impacts, delivering research outcomes to customers globally within months.Role OverviewKey ResponsibilitiesDesign and optimize large-scale distributed learning pipelines for multimodal embedding models.Enhance the inference performance of embedding models in production environments (throughput, latency, cost-efficiency).Design and build vector search systems and embedding serving infrastructure.Improve and automate the ML pipeline covering model development, training, and serving to facilitate rapid transitions to production.Address applied research challenges such as data filtering and evaluation metric design to enhance model quality and user experience.Explore and experiment with AI-based development tools like Claude and Gemini to boost development productivity.Collaborate closely with Research, Product, and Infrastructure teams, taking ownership of the end-to-end process to deliver models to actual customers.Ideal Candidate ProfileResearch or development experience in computer vision, natural language processing, or multimodal learning.Proficiency in Python and PyTorch, with experience in model training within large-scale distributed environments.Experience with embedding models, vector search systems, and advanced ML techniques.

Join Our Team!The Machine Learning Backend Engineer will be part of the ML Service Team, developing backend servers and common service platforms for various AI products within Toss Bank.The ML Service Team consists of Data Scientists, responsible for model development and application, and ML Engineers, who focus on server development, collaboratively creating innovative AI solutions.We work closely with product organizations responsible for banking services, leveraging AI technology to develop products. Collaboration with POs, designers, and developers ensures we manage the complete development cycle of AI products.Your Responsibilities:Design and develop robust server architecture to ensure a stable delivery of diverse AI products handled by the ML Service Team.Create individual services capable of handling large-scale traffic based on a solid server architecture.Develop servers that can efficiently serve heavy computational AI models with reliable response times and throughput.Operate services that allow distributed tracing in a microservices architecture while minimizing disruptions.We Are Looking For:Experience in server development and operation using one or more programming languages such as Python, Kotlin, or Go.Ability to design resilient service architectures and implement stable code structures.Experience deploying and monitoring servers in Kubernetes environments for stable operations is a plus.Experience and skills in developing and operating production services that handle high traffic or have stringent SLAs are desirable.A background in solving large and complex problems through software technology or creating business impact through services or products is advantageous.No prior ML experience is required, but a strong interest in AI/ML and a desire to gain experience in this area is welcomed.Resume Recommendations:Provide concrete examples of practical server development experience in your resume.Include details about technical considerations and your actual implementations while solving problems.If you have experience comparing costs and benefits among choices when balancing architectural complexity and development resources, please include that.Detail any projects that have made a business impact.Discuss experiences where you applied developed products in production and refined them based on performance metrics.Your Journey to Joining Toss Bank:Application submission > Live coding test > Job interview...

Who We AreWe are on the lookout for exceptional talent to help shape the global standard of video understanding AI!At TwelvaLabs, we are creating world-leading AI models specifically designed to process vast video datasets, enabling specialized functionalities such as search, analysis, summarization, and insight generation.Our models are utilized by the largest sports leagues globally to swiftly and accurately curate highlights from extensive game footage, providing an ultra-personalized viewing experience. In South Korea, integrated command centers rely on TwelvaLabs to efficiently navigate CCTV footage for rapid crisis response, while major broadcasters and studios worldwide leverage our models to produce content for billions of viewers.TwelvaLabs, a deep tech startup with offices in San Francisco and Seoul, has been recognized for four consecutive years as one of the top 100 AI startups globally by CB Insights. We have secured over $110 million in investments from premier VCs and companies including NVIDIA, NEA, Index Ventures, Databricks, and Snowflake. Our AI models, uniquely developed in Korea, are exclusively serviced through Amazon Bedrock. We are committed to creating innovative products alongside outstanding colleagues and growing together with our global clientele.At TwelvaLabs, we operate based on core values that include:A commitment to honesty and self-reflection about ourselves and our teamResilience and humility in the face of failure and feedbackA dedication to continuous learning to enhance the team's capabilitiesIf you enjoy solving challenging problems and growing through the process, the opportunity awaits you here at TwelvaLabs.About the TeamYou will be joining the team responsible for multimodal representation learning and production serving at TwelvaLabs. We develop models that integrate various modalities such as video, audio, and text into a unified embedding space, reliably serving them in production systems used by thousands of clients worldwide.Our responsibilities include conducting experiments on multimodal embedding models within a large-scale distributed learning environment and transitioning research outcomes into real-time inference systems. With access to top-tier GPU resources like the NVIDIA B300, we minimize the transition cycles from research to production.Our research results reach global clients within months, and we collaborate closely with the Research, Product, and Infrastructure teams to create significant technical impacts.About the RoleAs a Senior Machine Learning Engineer on the Embedding & Search team, you will play a pivotal role in developing and owning critical components of TwelvaLabs' search and retrieval platform. This platform integrates vector search, lexical retrieval, and reranking to deliver fast, accurate, and scalable search experiences for our clients.This position demands expertise in systems-heavy ML engineering, focusing on information retrieval, ML serving, and distributed systems. We seek a strong engineer capable of deconstructing well-defined problems with moderate ambiguity into concrete milestones, delivering reliable and high-performance solutions.Key ResponsibilitiesDevelop and manage essential subsystems of our search platform on EKS, including vector indexing (ANN), lexical retrieval, hybrid fusion, reranking, and temporal (segment-level) search.Enhance retrieval performance across vector and lexical paths at a scale ranging from millions to billions.

Join Coupang as a Senior Staff Machine Learning Engineer in our Eats Search & Discovery team!In this role, you will leverage cutting-edge machine learning techniques to enhance our search capabilities and improve the customer experience. Your expertise will guide the development of innovative algorithms that power our platform.Key Responsibilities:Design and implement scalable machine learning models.Collaborate with cross-functional teams to integrate models into production.Analyze data to improve algorithms and enhance performance.Mentor junior engineers and contribute to the team’s knowledge base.

# About the Team- Join us in enhancing customer experiences using data and machine learning within the Toss Commerce domain.- Toss consists of multiple domains, each operating as a complete business, including Commerce, Pay, Financial Marketplace, Ads & Benefit, Foundation, and Growth.- The Commerce domain has been the fastest-growing segment of Toss.- Initially launched to activate Toss Pay and expand transaction volumes, Toss Commerce has since introduced Toss Shopping and seller admin services, along with shopping ad services, providing new value to both users and sellers.- Within the Commerce domain, various teams operate like startups, including Shopping Silo, Shopping Platform Team, Shopping Ads Silo, and Commerce Business Team.- As we move into 2024, we plan to lay the groundwork for significant growth by launching diverse features within Commerce.- **Curious about Toss's Data Organization?** [→ *Toss Data Division Wiki*](https://recruit-data-division.oopy.io/)

Join Our Innovative TeamThe Machine Learning Engineer (Infra) will be part of the ML Platform Team within the Product Division at Toss Securities.The primary goal of the ML Platform Team is to create an optimal machine learning platform that enables the efficient and stable development and operation of various AI/ML services at Toss Securities.The ML Engineer (Infra) will focus on maximizing the efficiency of large-scale AI infrastructure, finely controlling resource usage, and enhancing infrastructure performance to its peak. Your Responsibilities Design and operate high-performance AI computing environments reliably.Design and operate top-of-the-line GPU clusters (H100, B300 series) connected via InfiniBand and high-performance storage (400Gbps) within a Kubernetes environment.Beyond merely building infrastructure, optimize networks and storage to extract the full potential of hardware performance. Develop a comprehensive control system for the entire AI infrastructure.Create an observability system to integrate and monitor AI resources distributed across internal infrastructure and external cloud.Implement management features to prevent resource monopolization by specific services and allocate resources precisely based on importance. Create automation tools for the most efficient resource usage.Analyze actual usage patterns to develop tools that recommend 'just-right resources' to avoid waste.Implement features that automatically scale up or down based on real-time model performance or error rates, and reallocate GPUs where necessary. Establish an environment for identifying and resolving model performance bottlenecks.Build profiling environments to accurately pinpoint slowdowns during model training or serving.Support the analysis and improvement of performance degradation causes between hardware and software. Who We Are Looking ForYou have experience building and operating Kubernetes-based ML infrastructures that handle large-scale traffic.You take responsibility for reliably operating live services beyond simple development.You have experience persistently analyzing and debugging to resolve root causes when issues arise.You possess a solid understanding of system resources (GPU/CPU/Memory/Network/Storage) and have experience building monitoring systems for them.You value the process of solving various problems that arise during service operations and strengthening the system. Preferred QualificationsExperience in unified monitoring of resource usage in large-scale clusters.Experience building systems to systematically control resources through Quota and Rate Limits.Experience with open-source platforms like Kubeflow or Kubernetes, including in-depth modifications as needed.Experience analyzing and optimizing bottlenecks at the kernel level using tools like Nsight Systems/Compute or PyTorch Profiler.Experience designing tasks to reduce costs or enhance performance tailored to workload characteristics (Rightsizing, Cost Optimization).Experience leveraging GPU virtualization technologies like MIG and MPS to maximize resource utilization.

About the Team You'll Join The ML Engineer (Platform) at Toss Securities is part of the ML Platform Team within the Product Division. The mission of the ML Platform Team is to create an optimal machine learning platform that enables efficient and stable development and operation of various AI/ML services at Toss Securities. Your Responsibilities Upon Joining Develop and enhance the Gateway system, the gateway for ML services. Develop and operate a Gateway system based on FastAPI that handles enterprise-level LLM API requests. Design and implement authentication, routing, traffic control, fault isolation (Circuit Breaker, Fallback), large-scale TPS processing, and load balancing strategies from both application and infrastructure perspectives in the FastAPI-based Gateway application. Manage and serve ML services. Directly operate a machine learning model serving system in a Kubernetes environment. Design and improve the LLM serving architecture to operate stably under large traffic conditions. Monitor latency, error rates, resource usage, and analyze and resolve operational issues for the models in service. Identify root causes of failures and implement structural improvements, including operational policies and architecture. Develop and manage a common ML platform for the company. Develop and manage a common platform for efficiently operating the training and serving of internal ML/LLM models based on Kubeflow. Continuously monitor and optimize the performance and resources of workloads executed on the platform. Build infrastructure for LLM-based services. Operate LLM services using various serving frameworks such as vLLM, SGLang, and Triton. Manage the environment to ensure stable operation of training and serving workloads on high-performance GPU clusters like H100/B300. Build and operate a large-scale data training environment for finance domain-specific LLMs. We Are Looking for Candidates Who: Are proficient in one or more programming languages such as Python, Go, Java, or Kotlin, and have experience designing and developing API servers in production environments. Have experience developing or operating API Gateways (Nginx, Kong, etc.) or LLM Routers (LiteLLM, Envoy AI Gateway, etc.), with a background in handling high-volume traffic and incident response. Have experience operating serving logs and event pipelines integrated with Kafka, Elasticsearch, and Kibana. Have experience defining monitoring metrics for model serving and configuring and operating dashboards using Prometheus and Grafana. Have experience operating ML/LLM model serving using KServe, BentoML, vLLM, SGLang, etc. Have experience directly managing MLOps components (Kubeflow, KServe, Airflow, Argo CD, MLflow, etc.) in Kubernetes environments and debugging and resolving issues. Can design and apply long-term improvement plans through root cause analysis beyond short-term responses to issues that arise during service operations. Additional Preferred Experience: Experience in related fields or technologies will be a plus.

Who We AreAt TwelveLabs, we are at the forefront of creating innovative multimodal foundation models capable of interpreting videos in a manner akin to human understanding. Our groundbreaking models have set new benchmarks in video-language modeling, granting us enhanced capabilities and fundamentally reshaping how we engage with and analyze diverse media formats.With over $110 million in Seed and Series A funding, we are supported by leading venture capital firms including NVIDIA’s NVentures, NEA, Radical Ventures, and Index Ventures, alongside esteemed AI pioneers like Fei-Fei Li, Silvio Savarese, and Alexandr Wang. Our headquarters are in San Francisco, complemented by a significant presence in the APAC region from our Seoul office, reflecting our dedication to fostering global innovation.Our strategic partnerships with NVIDIA and AWS provide us with access to state-of-the-art hardware, including B300s, enabling us to explore the frontiers of video AI technology.As a global organization, we celebrate the distinct journeys of each individual. Our diverse cultural, educational, and life backgrounds empower us to consistently challenge traditional norms. We seek passionate individuals who resonate with our mission and are eager to contribute to transformative advancements in technology. Join us in revolutionizing video comprehension and multimodal AI.About the TeamThe Pegasus team is integral to TwelveLabs' video understanding initiatives and spearheads the development of Pegasus, our Video Analysis product. We focus on constructing multimodal video analysis systems that excel in instruction following and generate complex, hierarchically organized outputs. Our priority is to deliver products that hold real-world significance rather than engaging in isolated research, collaborating within a goal-driven, cross-functional team comprising both ML researchers and engineers.Our responsibilities encompass a wide array of challenges: large-scale distributed training of multimodal LLMs from pre-training to reinforcement learning, precise temporal segmentation and structured metadata extraction for practical applications, extending temporal context lengths to several hours, and implementing data curation processes that facilitate well-aligned evaluations and performance enhancements through improved training data.Our team utilizes the latest cutting-edge chips, including NVIDIA B300s, to propel the limits of video analysis systems—accelerating our transition from research to production as swiftly as possible.

# Join Our TeamThe Machine Learning Engineer (Service) is part of the ML Tribe within Tossbank's Data Division.Our team is dedicated to leveraging AI (LLM, ML) technologies to tackle challenges previously unresolved in the financial sector, optimizing operations with the aim of maximizing profitability.Your ResponsibilitiesDevelop and operate agents to optimize financial tasks.Design and develop systems for delivering robust and scalable financial ML models.We Are Looking For Someone WhoHas experience and skills in developing and operating ML services with high SLA requirements in high-traffic environments.Can design service architectures that consider both stability and scalability, alongside writing maintainable, high-quality code.Has experience or interest in optimizing tasks with agents.Is familiar with technologies such as FastAPI, LLM, Agents, Kafka, Spark, Flink, and Airflow.Has a track record of utilizing AI technologies to solve large and complex problems or has created services/products that have made a significant business impact.Resume TipsStructure your resume around practical development experiences with specific examples.Detail your technical considerations and implementations aimed at solving problems.Discuss your experiences in analyzing trade-offs between architectural complexity and development resource allocation to determine cost efficiency.If you have worked on projects that resulted in business impacts, please elaborate on those experiences.Share your experiences in applying developed products to production and refining them based on performance metrics.Journey to Joining TossbankApplication Submission > Live Coding Test > Technical Interview > Cultural Fit Interview > Reference Check > Offer Negotiation > Final Acceptance and OnboardingImportant NotesJob offers may be rescinded if any false information is found in your resume or if disciplinary actions are noted in your employment history.According to Tossbank's Employment Regulations Article 8 (Hiring Disqualifications), candidates found to have disqualifying reasons may have job offers revoked.Applicants with disabilities or those eligible for national veterans' benefits are given preferential treatment in accordance with relevant laws.

-Legal Entity: Hyperconnect-Brand: Match Group AIAbout Match Group AIMatch Group AI is a pioneering technology organization dedicated to leveraging artificial intelligence to solve core challenges in online dating. We focus on critical areas of user experience, such as profiles, matching algorithms, and Trust & Safety, to unearth problems and innovate user experiences. By employing the latest AI technologies and data-driven methodologies, we are transforming how users interact with our platforms. Furthermore, we collaborate with global dating brands like Tinder and Hinge to enhance our shared technological foundation.To learn more about our ongoing projects, please check out this article. "Introducing the Match Group AI Team." Meet the Match Group AI ML TeamOur ML Team is composed of skilled Machine Learning Engineers who apply AI/ML technologies across Match Group’s diverse services. Our work begins by identifying and defining problems that emerge during the product development and operational phases. We develop or replicate the most suitable State-of-the-Art (SotA) models and deploy them reliably and efficiently in mobile and server environments. Through continuous monitoring and enhancements, we build the AI Flywheel for our services. This process involves close collaboration with backend, frontend, and DevOps engineers, data analysts, and project managers to create impactful AI experiences for actual users. For more insight into our working dynamics, please refer to the following resources.- [How AI Lab Works] Interview with Head of AI - Shurain- AI in Social Discovery (Blending Research and Production)Some of our work achievements are shared externally as papers or open-source code. When creating ML models for product use, existing research often falls short. To address these gaps, project participants collaboratively refine the research outcomes, and if possible, share the code. As a result, we have achieved approximately 20 notable research contributions, including the following.- 2024 CUPID: Real-time Session-based Mutual Recommendation System for 1:1 Social Discovery Platform - presented at ICDM Workshop- 2023 TiDAL: Active Learning Techniques Based on Model Behavior for Efficient Learning Processes - published at ICCV 2023- 2023 ...