Machine Learning Engineer - Personalization & Recommendation Systems

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.

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.

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.

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

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.

Pinterest is looking for a PhD Fall Machine Learning Intern with a focus on visual, multimodal, and recommender systems. This internship centers on supporting advanced machine learning projects alongside skilled engineers and researchers. Role overview The position involves contributing to ongoing research and development in machine learning. Interns will have the chance to work on projects that explore visual understanding and recommendation technologies, learning from experienced team members throughout the process. Collaboration Expect to work closely with engineers and researchers who specialize in machine learning. The environment encourages sharing ideas and building solutions that impact Pinterest’s products. Locations San Francisco, CA, US Palo Alto, CA, US Seattle, WA, US New York, NY, US

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.

Join the Sleep Fitness RevolutionAt Eight Sleep, we are dedicated to unlocking human potential through the power of optimal sleep. As pioneers in the sleep fitness domain, we are transforming the concept of well-being by developing cutting-edge hardware, software, and AI technologies designed to enhance sleep quality. Our innovative products are engineered to maximize mental, physical, and emotional performance, turning each night into a tailored, data-driven recovery session.Trusted by elite athletes and health-conscious individuals across over 30 countries, Eight Sleep has been recognized as one of Fast Company’s Most Innovative Companies in 2019, 2022, and 2023, as well as featured twice in TIME's “Best Inventions of the Year.” Our team operates like a high-performance unit: agile, focused, and driven by impactful results. We prioritize refining and iterating on our offerings to enhance our members' sleep experiences and empower them to wake up rejuvenated.Every position at Eight Sleep offers an opportunity to contribute to groundbreaking technology, collaborate with exceptional talent, and influence a future where sleep is a proactive element of living well. If you are passionate about pushing boundaries and creating innovative solutions, this is your chance to make a difference in how the world experiences sleep and its potential.High Standards. No Compromises.At Eight Sleep, we operate with intensity and commitment, reflecting the mindset of top performers. We embrace a relentless focus on excellence in our endeavors, akin to the mamba mentality applied to innovative ideas and next-gen technology. We are not just about meeting expectations; we strive to exceed them, working diligently not out of obligation, but from a passion for impactful work. If you flourish under pressure and seek to engage in the most meaningful projects of your career, you will find a home here. If you desire an easier path, this may not be the right place for you.The RoleWe are in search of a Machine Learning Engineer to develop and deploy consumer-oriented AI systems that enhance personalization, coaching, and next-gen “sleep intelligence.” You will collaborate across data science, modeling, product development, and engineering to convert research insights into tangible, measurable improvements for our members.This role is perfect for individuals who thrive on end-to-end ownership, from defining problems and prototyping to offline evaluations, online experimentation, production deployment, and continuous iteration.

Join us in creating the most dependable on-demand logistics engine for last-mile retail delivery! We are on the lookout for a seasoned machine learning engineer to aid in the development of cutting-edge growth and personalization models that will elevate DoorDash's expanding retail and grocery services.About the RoleWe are seeking a dedicated Applied Machine Learning expert to become part of our innovative team. As a Staff Machine Learning Engineer, you will conceptualize, design, implement, and validate algorithmic enhancements that enrich the growth and personalization experiences central to our rapidly evolving grocery and retail delivery business. Leveraging our advanced data and machine learning infrastructure, you will implement novel ML solutions to enhance the consumer search experience, making it more relevant, seamless, and enjoyable across grocery, convenience, and various retail sectors. A strong command of production-level machine learning and proven experience in addressing end-user challenges while collaborating effectively with multidisciplinary teams is essential.This position will report to the engineering manager on our Personalization team and is expected to be hybrid, combining both in-office and remote work (#LI-Hybrid).

Join our dynamic Personalization team at Boomtrain as a Machine Learning Engineer. We are in search of a skilled engineer who will play a pivotal role in developing and enhancing our recommendation systems that cater to a variety of customers.In this role, you will collaborate with a talented team dedicated to designing and implementing innovative models and systems that deliver personalized recommendations. You will have the opportunity to work on complex engineering challenges and contribute to generating hundreds of millions of recommendations daily.This position offers a unique chance to engage in end-to-end project work and make a significant impact on our personalization initiatives.Key Responsibilities:Research and propose advanced recommendation and optimization models to enhance our personalization systems.Develop and maintain offline model generation pipelines.Design and maintain online recommendation serving systems.

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.

At Lyft, our mission is to connect and serve communities by fostering an inclusive work environment where every team member feels valued and empowered to excel.With over half a billion rides completed, Lyft is tackling complex challenges in a fast-evolving landscape filled with extensive data and innovative solutions across various domains including Marketplace, Mapping, Fraud Prevention, Trust & Safety, and Growth. As we redefine transportation with our next-generation platform, we utilize advanced machine learning techniques that process peta-byte scale data to create low-cost, ultra-immersive transportation solutions that enhance lives. Our dedicated Machine Learning Engineers are at the forefront of these efforts, crafting solutions that significantly influence our core business operations.If you are a critical thinker with a robust background in machine learning workflows, passionate about leveraging data to solve business challenges, and thrive in a dynamic, collaborative setting, we want to hear from you!As a Senior Machine Learning Engineer, you will design and implement algorithms that drive the core services and influential products of our platform. The range of challenges we tackle is remarkably diverse, spanning transportation, economics, forecasting, mapping, safety, personalization, and adaptive control. We are eager to welcome motivated experts in these fields who are excited about developing reliable ML systems and solving problems through data in an innovative and fast-paced environment.

Join Scale AI's ML platform team (RLXF) as a Machine Learning Research Engineer, where you will play a pivotal role in developing our advanced distributed framework for training and inference of large language models. This platform is vital for enabling machine learning engineers, researchers, data scientists, and operators to conduct rapid and automated training, as well as evaluation of LLMs and data quality.At Scale, we occupy a unique position in the AI landscape, serving as an essential provider of training and evaluation data along with comprehensive solutions for the entire ML lifecycle. You will collaborate closely with Scale's ML teams and researchers to enhance the foundational platform that underpins our ML research and development initiatives. Your contributions will be crucial in optimizing the platform to support the next generation of LLM training, inference, and data curation.If you are passionate about driving the future of AI through groundbreaking innovations, we want to hear from you!

Join Anthropic as a Machine Learning Systems Engineer within our Encodings and Tokenization team, where you'll play a pivotal role in refining and optimizing our tokenization systems across Pretraining and Finetuning workflows. By bridging the gap between our Pretraining and Finetuning teams, you will help shape the essential infrastructure that enhances how our AI models learn from diverse data. Your contributions will be crucial in ensuring our AI systems remain reliable, interpretable, and steerable, driving forward our mission of developing beneficial AI technologies.

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 Our TeamJoin the innovative Sora team at OpenAI, where we are at the forefront of developing multimodal capabilities for our foundation models. Our hybrid research and product team is dedicated to seamlessly integrating multimodal functionalities into our AI solutions, ensuring they are dependable, user-centric, and aligned with our vision of benefiting society at large.Role OverviewAs a Machine Learning Engineer specializing in Distributed Data Systems, you will be instrumental in designing and scaling the infrastructure that facilitates large-scale multimodal training and evaluation at OpenAI. Your role will involve managing complex distributed data pipelines, collaborating closely with researchers to convert their requirements into robust, production-ready systems, and enhancing pipelines that are essential for Sora's rapid iteration cycles.We are seeking detail-oriented engineers with extensive experience in distributed systems who thrive in high-stakes environments and excel in building resilient infrastructure.This position is located in San Francisco, CA, and follows a hybrid work model, requiring three days in the office each week. We also provide relocation assistance for new team members.Key Responsibilities:Design, implement, and maintain data infrastructure systems, including distributed computing, data orchestration, distributed storage, streaming infrastructure, and machine learning systems, with a focus on scalability, reliability, and security.Ensure our data platform can scale exponentially while maintaining high reliability and efficiency.Collaborate with researchers to gain a deep understanding of their requirements, translating them into production-ready systems.Strengthen, optimize, and manage critical data infrastructure systems that support multimodal training and evaluation.You Will Excel in This Role If You:Possess strong experience with distributed systems and large-scale infrastructure, coupled with a keen interest in data.Exhibit meticulous attention to detail and a commitment to building and maintaining reliable systems.Demonstrate solid software engineering fundamentals and effective organizational skills.Thrive in environments characterized by ambiguity and rapid change.About OpenAIOpenAI is a trailblazing AI research and deployment organization committed to ensuring that general-purpose artificial intelligence serves humanity. We continuously push the boundaries of AI capabilities and strive to create technology that benefits everyone.

Join Us in Building a Safer World.At TRM Labs, we specialize in blockchain analytics and AI solutions aimed at assisting law enforcement, national security agencies, financial institutions, and cryptocurrency businesses in identifying, investigating, and preventing crypto-related fraud and financial crime. Our innovative platforms leverage blockchain intelligence and AI technology to trace funds, detect illicit activity, and construct comprehensive threat profiles. Trusted by leading organizations worldwide, TRM Labs is committed to enabling a safer and more secure environment for all.Our AI Engineering Team is dedicated to pioneering next-generation AI applications, particularly in the realm of Large Language Models (LLMs) and agentic systems. Our goal is to develop resilient pipelines and high-performance infrastructure that facilitate the swift, safe, and scalable deployment of AI systems.We manage extensive petabyte-scale pipelines, ensuring model serving with millisecond latency while providing the necessary observability and governance to make AI production-ready. Our team actively evaluates and integrates leading-edge tools in the LLM and agent space, including open-source stacks, vector databases, evaluation frameworks, and orchestration tools to accelerate TRM’s innovation pace.As a Senior or Staff ML Systems Engineer – LLM, you will play a pivotal role in constructing and scaling our technical infrastructure for AI/ML systems. Your responsibilities will include:Creating reusable CI/CD workflows for model training, evaluation, and deployment, integrating tools such as Langfuse, GitHub Actions, and experiment tracking.Automating model versioning, approval processes, and compliance checks across various environments.Developing a modular and scalable AI infrastructure stack that encompasses vector databases, feature stores, model registries, and observability tools.Collaborating with engineering and data science teams to embed AI models and agents into real-time applications and workflows.Continuously assessing and incorporating state-of-the-art AI tools (e.g., LangChain, LlamaIndex, vLLM, MLflow, BentoML).Promoting AI reliability and governance while enabling experimentation, ensuring compliance, security, and continuous uptime.Enhancing AI/ML Model Performance and ensuring data accuracy and consistency, leading to improved model training and inference.Implementing infrastructure to facilitate both offline and online evaluation of LLMs and agents.

Join Ando Technologies as a Machine Learning Engineer specializing in AI-native systems and forecasting. In this role, you will leverage cutting-edge machine learning algorithms to develop predictive models and enhance our AI-driven solutions. Collaborate with cross-functional teams to transform data into actionable insights and drive strategic decisions. Ideal candidates will have a passion for innovation and a strong understanding of AI technologies.

Join Orchard as a Machine Learning Engineer and play a pivotal role in transforming data into actionable insights. In this dynamic position, you will leverage your expertise in machine learning algorithms and data analysis to develop innovative solutions that enhance our products and services.We are looking for a proactive team player who thrives in a fast-paced environment and possesses strong problem-solving skills. You will collaborate with cross-functional teams, engage with large datasets, and contribute to the design and implementation of machine learning models.

OverviewPluralis Research is at the forefront of innovation in Protocol Learning, specializing in the collaborative training of foundational models. Our approach ensures that no single participant ever has or can obtain a complete version of the model. This initiative aims to create community-driven, collectively owned frontier models that operate on self-sustaining economic principles.We are seeking experienced Senior or Staff Machine Learning Engineers with over 5 years of expertise in distributed systems and large-scale machine learning training. In this role, you will design and implement a groundbreaking substrate for training distributed ML models that function effectively over consumer-grade internet connections.