PhD Fall Machine Learning Intern in Visual and Recommender Systems

Join our dynamic team at Liquid AI as a Member of Technical Staff where you will leverage your expertise in applied machine learning and recommendation systems to drive innovative solutions. You will collaborate with a talented group of professionals in a fast-paced environment, contributing to the development of advanced algorithms that enhance user experience and operational efficiency.

About KreaKrea is at the forefront of developing advanced AI creative tools designed to enhance and empower human creativity. Our mission is to create intuitive and controllable AI solutions that allow creatives to express themselves across various formats including text, images, video, sound, and 3D.About the PositionWe are seeking a talented Machine Learning Engineer to lead the design and implementation of Krea’s personalization and recommendation systems from the ground up. You will take full ownership of how we comprehend user preferences, curate engaging content, and customize generative models to reflect individual aesthetics.This role sits at the exciting intersection of recommendation systems, representation learning, and generative imaging and video technologies.Your ResponsibilitiesLead the architecture and development of Krea’s personalization and recommendation framework, overseeing the technical direction from inception to deployment.Craft algorithms that effectively model user preferences and tastes, enabling our systems to adapt to individual styles and aesthetics.Develop high-quality, curated feeds that strike a balance between exploration, personalization, and aesthetic coherence.Collaborate closely with our model and research teams to co-create personalization mechanisms that shape how our generative models learn, adapt, and express creative styles.Contribute to research in personalized image generation, with a focus on style, taste, and subjective quality.Work in tandem with product, design, and research teams to define what “good personalization” means in a creative context.Take systems from initial research and prototyping stages through to production, ongoing iteration, and enhancement.

As a Technical Staff Member specializing in Machine Learning, you will:Engage in the complete development lifecycle of innovative large-scale deep learning models.Curate datasets, architect solutions, implement algorithms, and train and assess models to enhance our offerings.Work collaboratively with engineers and researchers to convert groundbreaking research into real-world applications.Join us at a pivotal time, take on diverse roles, and contribute to building transformative products from the ground up!

At Gimlet Labs, we are pioneering the development of the first heterogeneous neocloud designed specifically for AI workloads. As the demand for AI systems surges, traditional homogeneous infrastructures face critical limits in power, capacity, and cost. Our innovative platform effectively decouples AI workloads from their hardware foundations, intelligently partitioning tasks and orchestrating them to the most suitable hardware for optimal performance and efficiency. This strategy fosters heterogeneous systems that span multiple vendors and generations, including cutting-edge accelerators, enabling significant enhancements in performance and cost-effectiveness at scale.In addition to this foundational work, Gimlet is establishing a robust neocloud for agentic workloads. Our clients benefit from deploying and managing their workloads via stable, production-ready APIs, without the need to navigate hardware selection or performance optimization intricacies.We collaborate with foundation labs, hyperscalers, and AI-native companies to drive real production workloads capable of scaling to gigawatt-class AI datacenters.We are currently seeking a Member of Technical Staff specializing in ML systems and inference. In this pivotal role, you will be responsible for designing and constructing inference systems that facilitate the execution of complete models in real production environments. You will operate at the intersection of model architecture and system performance to ensure that inference processes are swift, predictable, and scalable.This position is perfect for engineers with a deep understanding of modern model execution and a passion for optimizing latency, throughput, and memory utilization across the entire inference lifecycle.

At Philo, we are a dedicated team of technology and product enthusiasts committed to reshaping the television landscape. We blend cutting-edge technology with the captivating medium of television to create the ultimate viewing experience. Our mission is to enhance streaming capabilities through innovative cloud delivery and sophisticated machine learning algorithms that personalize content discovery. As a Senior Machine Learning Engineer specializing in Recommendation Systems, you will be at the forefront of our content personalization initiatives, significantly enhancing user engagement and satisfaction. Your expertise will help ensure that every time users open the Philo app, they find something they want to watch. In this pivotal role, you will spearhead the development of advanced algorithms and large-scale systems that drive Philo's recommendation engine. Collaborating closely with data science, product, infrastructure, and backend engineering teams, you will tackle complex machine learning challenges and develop innovative, data-driven solutions that enhance content discovery and foster user retention.

Join Reka as a Member of the Technical Staff in Applied AI!Leverage cutting-edge AI models to tackle intricate real-world challenges.Engage in close collaboration with researchers and fellow team members to explore the latest developments in AI and ML.Partner with our customers to seamlessly integrate our innovative models into their existing technology frameworks.Drive business success with a strong sense of product ownership and accountability.Be part of a pioneering team in a rapidly growing environment, taking on diverse roles.

At Tzafon, we are pioneering the development of scalable computing systems and pushing the boundaries of machine intelligence with our foundation model lab. Located in vibrant cities such as San Francisco, Zurich, and Tel Aviv, we have successfully secured over $12 million in funding to fuel our mission of expanding the horizons of AI technology.Our dynamic team comprises engineers and scientists with extensive expertise in machine learning infrastructure and research. Founded by IOI and IMO medalists, PhDs, and seasoned professionals from top tech firms, we specialize in training advanced models and constructing robust infrastructures to automate tasks across various real-world scenarios.In this role, you will collaborate closely with our product and post-training teams to deploy Large Action Models that drive impactful results. Your responsibilities will include building evaluation frameworks, establishing benchmarks, and creating fine-tuning pipelines to ensure optimal model performance.

Technical Staff Member in Applied AIAbout the OpportunityWe are seeking a highly skilled Technical Staff Member specializing in generative modeling to bridge the gap between our advanced models and the clients who rely on them. You will collaborate with a diverse team of machine learning experts, protein engineers, and biologists to revolutionize biological control and disease treatment. Your role will involve gaining a comprehensive understanding of our proprietary generative models and leveraging that expertise to deploy, adapt, and optimize these models in client environments, particularly within the pharmaceutical and biotech industries.This hybrid position requires a research-oriented mindset to deeply understand our models, paired with the communication skills necessary to translate that knowledge into production systems that yield scientific value for our collaborators.About UsAt Latent Labs, we are pioneering frontier models that decode the fundamentals of biology. Our ambitious goals are driven by curiosity and a commitment to scientific excellence. Prior to founding Latent Labs, our team co-developed DeepMind's Nobel Prize-winning AlphaFold, innovated latent diffusion, and created groundbreaking lab data management systems along with high-throughput protein screening platforms. Here, you will work alongside some of the brightest minds in generative AI and biology.We value interdisciplinary collaboration, continuous learning, and teamwork. Our team offsites foster a culture of trust and connection between our London and San Francisco offices. We are looking for innovators who are passionate about solving complex problems and making a positive global impact. Join us on our ambitious mission.Your QualificationsExpertise in Machine Learning: You are a proficient ML researcher with a strong background in generative modeling, evidenced by your contributions to notable open-source projects, impactful product launches, or significant publications in leading venues such as NeurIPS, ICML, ICLR, or Nature. You possess a deep understanding of generative model architectures, training dynamics, and inference behavior.Proficient ML Developer: You produce robust, tested, and maintainable ML code. Your experience includes using version control and code review systems. You are adept at rapid prototyping while also being able to write elegant production code. Additionally, you have experience in building systems that deploy large models via APIs and executing inference tasks in production environments.

About UsAt Applied Compute, we specialize in creating Specific Intelligence solutions for enterprises, developing agents that learn continuously from an organization’s processes, data, expertise, and objectives. We recognize a significant gap between the capabilities of AI models in isolation and their practical applications in real-world business contexts. Our systems often fall short because they lack adaptability to feedback. To address this, we are building a continual learning infrastructure that captures context, memory, and decision-making processes throughout the enterprise, enabling specialized agents to effectively execute real tasks.What Excites Us: We operate at a unique intersection where our product team constructs the platform that fuels a new generation of digital coworkers. Our research team pushes the boundaries of post-training and reinforcement learning, creating innovative product experiences. Our applied research engineers collaborate closely with clients to deploy models into production. This blend of strong product focus, deep research, and hands-on customer engagement is crucial for integrating AI into the enterprise. We are product-driven, research-informed, and actively engaged with our clients.Our Team: Our diverse team consists of engineers, researchers, and operators, many of whom are former founders. We have built RL infrastructure at leading organizations like OpenAI and Scale AI, and developed systems at Together, Two Sigma, and Watershed. We proudly serve Fortune 50 clients alongside companies like DoorDash, Mercor, and Cognition. Our work is supported by renowned investors, including Benchmark, Sequoia, and Lux.Who Thrives in Our Environment: We seek individuals eager to apply cutting-edge research and complex systems to tackle real-world challenges. You should be adept at quickly adapting to new environments, whether it’s a fresh codebase, a client’s data architecture, or an unfamiliar problem domain. A genuine enjoyment of customer interactions—listening, empathizing, and understanding how tasks are accomplished within their organizations—is essential. Those with entrepreneurial backgrounds, extensive side projects, or demonstrated end-to-end ownership typically excel in our company.

About SunoSuno is revolutionizing the music industry by harnessing the power of advanced AI technology to inspire creativity. Our innovative platform, which includes the groundbreaking Suno Studio, provides an exceptional generative audio workstation designed for everyone—from casual singers to aspiring songwriters and seasoned musicians. Suno is dedicated to empowering a diverse global community to create, share, and explore music, celebrating the joy of musical expression for all.About the RoleWe are seeking a visionary leader to spearhead our recommendations team at Suno. In this pivotal role, you will be at the forefront of developing our music discovery and recommendation systems, shaping how millions of users engage with music on our platform. Your expertise will drive the evolution of our systems while fostering a collaborative and innovative team environment.This position is ideal for an individual with extensive experience in scaling recommendation systems and a passion for crafting a superior user experience. If you are excited to apply your skills in a dynamic setting and lead a talented team, we want to hear from you!Discover more about this role at Suno!What You'll DoDefine and execute Suno's vision and strategy for recommendations, setting the technical direction for the team.Collaborate with leaders across product, engineering, and research to ensure our recommendations evolve in alignment with platform growth.Lead the design and development of a comprehensive recommendation system, from initial prototyping to large-scale deployment.Recruit, mentor, and expand a high-performing recommendations team.What You'll NeedA minimum of 5 years of experience in building large-scale recommendation systems, with at least 2 years in a leadership role overseeing development.Profound technical knowledge of cutting-edge technologies and methodologies in recommendation systems, along with a pragmatic approach to implementation.Exceptional collaborative skills with a proven ability to influence cross-functional teams.A genuine passion for Suno's mission and a keen interest in shaping the future of music discovery.Bachelor’s degree or equivalent experience.Additional Notes: Candidates must be eligible to work in the United States.This role requires onsite presence in San Francisco.

About Quizlet:At Quizlet, our vision is to empower every learner to achieve their educational goals in the most effective and enjoyable manner. As a thriving $1B+ educational platform, we serve two-thirds of U.S. high school students and half of college students, facilitating over 1 billion learning interactions weekly.By integrating cognitive science with advanced machine learning techniques, we tailor and enhance the learning experience for students, professionals, and lifelong learners alike. Our enthusiasm lies in the potential to support more learners through diverse methodologies and tools.Let's Shape the Future of Learning TogetherJoin us in designing and implementing AI-driven learning solutions that scale globally, unlocking the potential of learners everywhere.About the Team:The Personalization & Recommendations team is dedicated to crafting customized learning experiences that enable millions of learners to study more effectively. We are seeking Machine Learning Engineers across Senior to Staff levels (including Sr. Staff) to join our innovative team.You will leverage your expertise in modern recommender systems—encompassing deep learning-based retrieval, embeddings, and multi-stage ranking—to enhance Quizlet's personalization capabilities. Collaborating at the nexus of machine learning, product development, and scalable systems, you will ensure our recommendations are efficient, ethical, and aligned with learner outcomes, privacy, and fairness.This is an onsite position, requiring team members to work in the office at least three days a week: Monday, Wednesday, and Thursday, as well as additional days as needed. We believe this in-office collaboration fosters efficiency, enhances teamwork, and promotes both personal and organizational growth.

Overview: Join Listen Labs as we respond to a surge in market demand with an ambitious 6-month product roadmap. We are expanding our engineering team and are on the lookout for a highly skilled technical expert (our current team includes three IOI medalists) who is eager to build a transformative product that reshapes decision-making for businesses. If you have a passion for solving intricate problems from start to finish, we want to connect with you.About Listen LabsListen Labs is an AI-driven research platform designed to help teams quickly extract insights from customer interviews in a matter of hours rather than months. We empower our clients by enabling them to analyze conversations, identify key themes, and make faster, more informed product decisions.Why Work with Us?Exceptional Team: Founded by seasoned entrepreneurs with a successful AI exit, along with talent from renowned companies such as Jane Street, Twitter, Stripe, Affirm, Bain, and Goldman Sachs, our team boasts impressive credentials including IOI and ICPC backgrounds.Rapid Growth: As a 40-person team backed by Sequoia Capital, we have achieved a remarkable growth trajectory, scaling from $0 to a $14 million run-rate in less than a year. We prioritize craftsmanship and thrive on collaboration with individuals who take ownership.Impressive Traction: We are experiencing rapid growth across various sectors, securing enterprise clients such as Google, Microsoft, Nestlé, and Procter & Gamble.Proven Performance: We maintain an industry-leading win rate driven by our uniquely differentiated product.Market Validation: We consistently attract customers from diverse segments, achieving six-figure contracts that facilitate quick expansions.Viral Product: Our interviews reach tens of thousands of viewers, promoting product-led growth, organic expansion, and daily interest from Fortune 500 companies.Technical Challenges Await:Research Agent Development:Unlike traditional software purchases, hiring McKinsey offers valuable opinions, expertise, and execution. We aim to provide users with an AI agent that possesses complete knowledge about our platform and best research practices, assisting them in project setup, interview conduction, and response analysis.Human Database Creation:One of our core offerings is the ability to identify target users effectively (e.g., "power users of ChatGPT and Excel"). We are in the process of building a comprehensive database that connects users with the insights they need.

SentiLink is at the forefront of transforming identity verification and risk management, enabling both institutions and individuals to conduct transactions with confidence. We are committed to revolutionizing the outdated and inefficient identity verification landscape in the United States, offering solutions that are ten times faster, more intelligent, and more precise.Our rapid growth reflects the significant traction we've garnered, with our real-time APIs successfully verifying hundreds of millions of identities, especially within the financial services sector, while swiftly expanding into other markets. SentiLink enjoys the backing of top-tier investors such as Craft Ventures, Andreessen Horowitz, NYCA, and Max Levchin.We have received accolades from major publications including TechCrunch, CNBC, Bloomberg, Forbes, Business Insider, PYMNTS, and American Banker, and have consistently ranked on the Forbes Fintech 50 list since 2023. Notably, we made history by being the first company to implement the eCBSV and provided testimony before the United States House of Representatives regarding the future of identity verification.SentiLink promotes a flexible working environment, offering various work arrangements ranging from fully remote to in-office. As a digital-first organization, we emphasize strong collaboration across teams in the U.S. and India. Our offices are located in cities including Austin, San Francisco, New York City, Seattle, Los Angeles, and Chicago in the U.S., along with Gurugram (Delhi) and Bengaluru in India. If you are near any of these locations, we encourage regular in-office engagement. Some positions are designed to be hybrid or in-office. For instance, our engineering team in India primarily operates from our Gurugram office.

About Faire Faire is an online wholesale marketplace focused on supporting independent retailers. By connecting small businesses with products from around the world, Faire aims to help local shops compete with major players like Walmart and Amazon. The company uses technology, data analytics, and machine learning to provide insights and tools that level the playing field for entrepreneurs everywhere. Faire’s work strengthens local economies by enabling independent businesses to thrive. The team values resourcefulness, intelligence, and a commitment to community. Those who believe in supporting local businesses will find a shared purpose here. Role Overview: Data Science Intern – Personalization & Recommender Systems This internship focuses on building and improving machine learning systems that power search, personalization, and recommendations for Faire’s marketplace. Interns will join a team dedicated to developing algorithms that help local retailers discover relevant products and compete with larger competitors. The team welcomes Master’s and PhD students with a background in recommender systems, personalization, or applied machine learning. Who We’re Looking For Strong interest in recommender systems and personalization Experience applying modern machine learning techniques to ranking or representation learning PhD candidates: a record of publications or submissions to top conferences (such as KDD, RecSys, ICML, NeurIPS, WWW, SIGIR) Master’s candidates: meaningful research projects, internships, or open-source contributions in related areas What You’ll Work On Design and build advanced recommender systems for product ranking and discovery Develop methods for user and item representation learning Collaborate with machine learning engineers to move research solutions into production Tackle personalization challenges that impact millions of recommendations each day Location San Francisco, CA

At Magic, we are driven by our mission to develop safe Artificial General Intelligence (AGI) that propels humanity forward in addressing the most critical challenges. We firmly believe that the future of safe AGI lies in automating research and code generation, allowing us to enhance models and tackle alignment issues more effectively than humans alone can manage. Our innovative approach combines cutting-edge pre-training, domain-specific reinforcement learning (RL), ultra-long context, and efficient inference-time computation to realize this vision.Position OverviewAs a Software Engineer within the Inference & RL Systems team, you will play a pivotal role in designing and managing the distributed systems that enable our models to function seamlessly in production, supporting extensive post-training workflows.This position operates at the intersection of model execution and distributed infrastructure, focusing on systems that influence inference latency, throughput, stability, and the reliability of RL and post-training training loops.Our long-context models impose significant execution demands, including KV-cache scaling, managing memory constraints for lengthy sequences, batching strategies, long-horizon trajectory rollouts, and ensuring consistent throughput under real-world workloads. You will be responsible for the infrastructure that ensures both production inference and large-scale RL iterations are efficient and dependable.Key ResponsibilitiesCraft and scale high-performance inference serving systems.Optimize KV-cache management, batching methods, and scheduling processes.Enhance throughput and latency for long-context tasks.Develop and sustain distributed RL and post-training infrastructure.Boost reliability across rollout, evaluation, and reward pipelines.Automate fault detection and recovery mechanisms for serving and RL systems.Analyze and eliminate performance bottlenecks across GPU, networking, and storage components.Collaborate with Kernel and Research teams to ensure alignment between execution systems and model architecture.QualificationsSolid foundation in software engineering and distributed systems.Proven experience in building or managing large-scale inference or training systems.In-depth understanding of GPU execution constraints and memory trade-offs.Experience troubleshooting performance issues in production machine learning systems.Capability to analyze system-level trade-offs between latency, throughput, and cost.

Crunchyroll serves a global community of anime fans, connecting over 100 million people across more than 200 countries and territories. The platform offers streaming, theatrical releases, games, merchandise, and events, all centered around anime stories and characters. Role overview The Senior Applied Scientist in Recommendation and Personalization leads scientific development for Crunchyroll’s recommendation, ranking, and decisioning systems. The focus is to help fans discover and engage with anime series, movies, manga, merchandise, and games. This position collaborates with Machine Learning Engineers, Product Managers, Engineering, Marketing, and Content teams to enhance Crunchyroll’s personalized experiences. What you will do Lead research and applied science projects in personalization, covering problem definition, data exploration, model development, evaluation, experimentation, and iteration. Define what outstanding personalization looks like for Crunchyroll across app and web interfaces, email campaigns, and integrated systems for video, ecommerce, and manga. Demonstrate the impact of personalization efforts using evidence and metrics. Partner with engineering teams to bring scientific solutions into production. Reporting structure This role reports to the Director of Data Science and Machine Learning within the Center for Data and Insights. Location and schedule This position is based in Los Angeles, California, or San Francisco, CA. Crunchyroll follows a hybrid schedule, with in-office attendance required three days per week: Tuesday, Wednesday, and Thursday.

Join the team at Mirendil as a Member of Technical Staff specializing in Machine Learning Systems. In this role, you will leverage your expertise to develop innovative solutions that enhance our ML frameworks and contribute to groundbreaking projects in the AI space. Collaborate with top talent in a dynamic environment that promotes creativity and technical excellence.

Join us at Foxglove, where we are revolutionizing the robotics industry by building robust data infrastructure for real-world applications.As robotics transitions from research environments to practical implementations in factories, warehouses, vehicles, and field operations, data becomes essential for engineers to troubleshoot failures, understand unexpected behaviors, and enhance robotic systems.At Foxglove, we provide the observability, visualization, and data infrastructure that enable robotics and autonomous systems teams to efficiently ingest, store, query, replay, and analyze extensive volumes of multimodal sensor data from live systems and production fleets.About the RoleWe are seeking a talented Applied Machine Learning Engineer with strong infrastructure insights to design, deploy, and scale the machine learning systems that power our data platform. In this impactful role, you will be responsible for optimizing production ML infrastructure—from enhancing inference pipeline throughput to establishing training and evaluation workflows. You will focus on high-priority challenges, such as developing retrieval applications for petabyte-scale multimodal robotics data, utilizing cutting-edge models to create high-performance search and data mining products, and fostering an internal ML flywheel for rapid iteration. This is a hands-on, application-driven position rather than a research-focused role.Key ResponsibilitiesDeploy and manage inference infrastructure for production ML workloads, focusing on model serving, scalability, and cost efficiency.Build and oversee vector database integrations and embedding applications to facilitate semantic search across various multimodal robotics data types (image, video, point cloud, and time series).Design and implement evaluation and training infrastructure to enhance model performance rapidly.Lead cloud architecture decisions and tools to optimize inference latency, throughput, cost, and reliability at scale.Collaborate closely with product engineers to deliver application-driven ML features that empower developers at the forefront of robotics and physical AI, steering clear of prototype experiments.Identify appropriate off-the-shelf solutions for production and determine when to build versus buy.

Shape the Future with AI InnovationAt asari.ai, we are on a mission to empower individuals to create intricate systems and tackle the world's most challenging problems with the aid of advanced AI agents that are both scalable and reliable.Our team boasts a history of publishing award-winning AI research and is supported by elite investors such as Eric Schmidt, Caltech, Jeff Dean, and JP Millon.We thrive in a fast-paced environment, leveraging first-principles thinking and purposeful building. We believe that exceptional outcomes arise when individuals take ownership, grow collectively, and share both the hurdles and victories.Your RoleDesign, train, and assess hybrid AI systems that excel at scale while making optimal trade-offs.Develop scalable data processing and machine learning frameworks.Address min-max challenges: maximizing output while minimizing resources.Enhance our productivity by eliminating operational and tooling constraints.Ideal Candidate ProfileYou find motivation in resolving complex real-world challenges.You possess a proven background in a technical field such as machine learning, computer science, physics, or mathematics.You demonstrate strong programming skills (in Python, C++) and mathematical proficiency.You exhibit robust conceptual and structured thinking abilities.You communicate clearly and effectively, both verbally and in writing.You are eager and capable of rapid learning.You embody a team-oriented spirit.You can independently organize, plan, prioritize, and execute tasks.You are driven by excellence, ownership, and a proactive mindset.Preferred QualificationsExperience with open-source projects, published research, or demonstrated expertise in machine learning.Familiarity with applying deep learning, reinforcement learning, unsupervised learning, and related techniques to large-scale problems.Experience with distributed computing and managing large datasets.Compensation and BenefitsAttractive salaryEquity optionsComprehensive health insurance (100% covered), dental (90% covered), and vision (90% covered). Dependent coverage is at 50%.