Machine Learning Engineer - Personalization & Recommendations

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

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.

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

Pinterest’s Advanced Technology Group is looking for a Machine Learning Intern at the master’s level for Fall 2026. This internship centers on visual search and recommendation systems, blending research and engineering in a collaborative environment. Interns may help launch new features for millions of users or take part in academic research projects. Role overview This 12-week paid internship runs from September 21 to December 11, 2026. Work may be remote or hybrid, with office locations available in San Francisco, Palo Alto, Seattle, or New York. Team placement determines whether the role is remote or hybrid. What you will do Build and launch new user features using internal datasets and machine learning techniques. Conduct research focused on visual understanding and recommendation systems. Work closely with engineering teams and take part in external collaborations or mentoring. Contribute to features that may reach millions of users or support research for academic publication. Locations San Francisco, CA Palo Alto, CA Seattle, WA New York, NY Remote (team dependent) Internship details 12-week program: September 21 to December 11, 2026 Paid internship Remote or hybrid work (based on team) Application note Applying to this position enters candidates into consideration for multiple machine learning internship roles across Pinterest’s ML teams. Submit only one application within the USA or Canada; multiple applications may delay review.

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 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.

PhD Internship: Machine Learning EngineerAbout HandshakeAt Handshake, we are revolutionizing career development in the AI economy. With a network of over 20 million skilled professionals and partnerships with 1,600 educational institutions and 1 million employers, including all Fortune 50 companies, Handshake is the go-to platform for career exploration, hiring, and skill enhancement. Join us as we help individuals transition from freelance opportunities to internships and full-time roles, connecting talent with opportunities at every career stage.Our unique position in the market has catalyzed impressive growth, with projections indicating that we will triple our ARR by 2025.Why You Should Join Handshake Now:Contribute to the evolution of careers in the AI economy on a global scale, making a tangible impact.Collaborate with top AI labs, Fortune 500 companies, and prestigious educational institutions.Play a key role in building a fast-growing business targeting multi-billion-dollar revenues.Position OverviewAs a Machine Learning Engineering Intern, you will be instrumental in crafting intelligent product experiences that assist students in discovering and securing opportunities. Your responsibilities will encompass search algorithms, recommendations, and matching systems that enhance job exploration on Handshake.This internship will provide you with practical experience in developing, assessing, and deploying machine learning models within a production environment, as well as insights into the design, optimization, and maintenance of large-scale ML systems.This is a paid, full-time summer internship with two cohort options:May 18 – August 7, 2026June 15 – September 4, 2026Key Responsibilities:Collaborate with experienced engineers and data scientists to develop machine learning models that enhance product features and improve user experience.Engage in experimentation, model evaluation, and performance monitoring.Participate in technical discussions, brainstorming sessions, and team reviews.Document methodologies and findings to facilitate knowledge sharing and long-term improvements to systems.QualificationsEssential Qualifications:Currently pursuing a PhD in Computer Science, Data Science, Machine Learning, or a related field.Strong programming skills in Python and familiarity with machine learning libraries (e.g., TensorFlow, PyTorch).Experience with data analysis and statistical modeling.Excellent problem-solving abilities and analytical thinking.Strong communication skills and ability to work collaboratively in a team environment.

Founded in 2007, Airbnb has transformed the way people travel, connecting over 5 million hosts with more than 2 billion guests worldwide. Our platform enables unique stays and experiences that promote authentic community connections. Join Our Team:We are looking for a Machine Learning Engineering Intern to be part of our 2026 Summer Internship Program.At Airbnb, machine learning is at the forefront of our innovation, addressing challenges in areas such as search ranking, payment security, fraud prevention, and user growth, all while ensuring a seamless user experience. Our team actively contributes to the ML community by publishing insightful articles and research at leading conferences. Explore our work on Medium: https://medium.com/airbnb-engineering/tagged/machine-learningThe Search Relevance and Personalization team focuses on developing cutting-edge algorithms and personalizing user experiences across various product surfaces. Our team of over 30 ML Engineers is dedicated to experimentation, frequently conducting A/B tests to enhance the relevance of our offerings. We also engage in applied research, regularly sharing our findings at prominent conferences such as KDD, CIKM, and WSDM. Your Impact:As a Machine Learning Intern, you will be immersed in a collaborative environment where you'll lead a project that aligns with your technical interests and career goals. Your project will explore innovative applications of AI agents to enhance Airbnb's search and discovery functionalities. Internship Experience:During your internship, you will be part of a dynamic team tackling significant challenges. Expect support, mentorship, and engaging activities that will enrich your learning experience and allow you to make a tangible impact on our products as we redefine connection, belonging, and travel.Take ownership of a capstone project from concept to completion.Collaborate with experienced engineers and leaders in the field.

Cheminformatics and Machine Learning InternGenesis Molecular AI is assembling a premier computational team dedicated to creating the industry’s most rapid and precise small molecule property predictions by harnessing the synergy of machine learning and physics-based methods. We invite early-career scientists who excel in the development and application of machine learning, physics, and cheminformatics techniques to advance our ambitious drug discovery initiatives.About the RoleIn this role, you will be an integral part of our machine learning and cheminformatics team, contributing significantly to our internal ML and physics-based platform that propels our drug discovery projects forward.Your responsibilities will likely encompass developing innovative methodologies for potency and ADME/PK property prediction, as well as prototyping integrations with cutting-edge molecular modeling tools.Your ResponsibilitiesLead a groundbreaking research project from concept to implementation, aimed at enhancing our internal tools for potency and ADME prediction.Prototype innovative approaches based on recent academic publications. Design and conduct large-scale experiments to validate promising findings using both internal and public benchmarks.Collaborate closely with our computer-aided drug discovery scientists and medicinal chemists to develop, benchmark, and implement enhancements to our drug discovery platform as it relates to our internal and partnered drug discovery efforts.Your ProfileA graduate student with a strong track record in developing cheminformatics tools and/or physics methods relevant to drug discovery.Proficient in Python programming with a demonstrated ability to navigate and contribute to complex codebases.An experienced machine learning practitioner with familiarity in common architectures and proven skills in troubleshooting real-world applications.A detail-oriented data scientist adept at managing diverse data sources. Familiarity with RDKit, OpenEye, and other cheminformatics libraries is a plus.Passionate about making a tangible impact on drug discovery projects and engaging with a diverse team of ML practitioners, medicinal chemists, and drug discovery scientists.

About NationGraphAt NationGraph, we are revolutionizing the accessibility and usability of public sector data for businesses targeting municipalities, state agencies, educational institutions, and specialized districts. Our advanced data intelligence engine extracts actionable insights from millions of public sector sources, empowering organizations to make informed decisions. Established in 2024, our mission is to democratize information, ensuring that public data is genuinely accessible to everyone. Discover more at nationgraph.comOur TeamComprises seasoned entrepreneurs who have successfully built, scaled, and exited multiple companies.Developed robust software infrastructure capable of processing billions in transactions.Supported by top-tier venture capitalists and seasoned operating partners with a track record of investing in and nurturing iconic brands.Role OverviewDesign and implement end-to-end machine learning pipelines.Extract and mine data from various online sources through large-scale web crawling and scraping techniques to enhance our models and insights.Convert unstructured text data into structured knowledge using natural language processing (NLP), entity recognition, and bespoke models.Develop and refine text classification models to systematically organize intricate datasets.Enhance retrieval-augmented generation (RAG) systems utilized in our product offerings.Drive our data strategy by identifying and integrating new data sources.Tackle open-ended technical challenges, fostering a culture of learning and collaboration within the team.Primarily utilize Python and SQL for development.QualificationsA strong quantitative background in fields such as computer science, physics, mathematics, or engineering.Solid foundation in mathematics and statistics.A PhD in a quantitative discipline.Expertise in Python programming.Proactive ownership mentality with the ability to address complex technical challenges to create commercial value.A genuine enthusiasm for continuous learning, growth, and uncovering insights from complex datasets.Strong problem-solving, communication, and collaboration abilities in a dynamic work 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!

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.

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.

OverviewAt Pulse, we are revolutionizing the way data infrastructure functions by addressing one of the most enduring challenges: the extraction of precise, structured information from complex documents at scale. Our innovative architecture integrates advanced schema mapping with finely-tuned extraction models, overcoming the limitations of traditional OCR and parsing techniques.As a dynamic and rapidly expanding team located in San Francisco, we empower Fortune 100 enterprises, YC startups, public investment firms, and growth-stage companies. Our impressive growth is supported by top-tier investors who believe in our vision.About the InternshipJoin us as a Machine Learning Engineer Intern, where you'll collaborate closely with our founding engineers to tackle core ML challenges that blend computer vision, natural language processing (NLP), and data infrastructure. This internship is tailored for ambitious second- or third-year undergraduate students eager to acquire hands-on experience with production-scale AI systems.Key ResponsibilitiesTrain and optimize OCR, layout, table, and vision-language models.Contribute to model evaluation, data curation, and active learning processes.Enhance inference, batching, and quantization techniques on GPU.Collaborate with engineers to ensure reliable productionization of models.Document insights that guide the future of our model roadmap.Required QualificationsCurrently pursuing a degree in Computer Science, Engineering, or a related discipline.Proficiency in Python and experience with frameworks such as PyTorch or JAX.Familiarity with contemporary vision or multimodal architectures.Strong programming skills with a keen interest in production systems.Preferred QualificationsExperience with distributed training or model optimization tools (e.g., Triton, TensorRT, ONNX).Contributions to open-source projects in ML, NLP, or CV.Compensation$40–$70 per hour based on experience.Includes a daily meal stipend, office perks, and mentorship from our founding team.

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 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.

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.

The OpportunityJoin us at ComfyOrg as a Senior/Staff Applied Machine Learning Engineer! We are on the hunt for a passionate innovator who is enthusiastic about optimizing model inference. You will play a pivotal role in developing the heart of ComfyUI, our cutting-edge visual AI platform. Your expertise will help us push the limits of AI model performance, making them run faster and more efficiently than ever before.Are You a Match?You are fascinated by model inference, memory management, and torch optimizations.You possess experience in writing production-level PyTorch code that challenges performance standards.You have a passion for understanding the inner workings of AI models.You thrive on developing highly optimized code that consistently delivers results.You believe that the current landscape of ML deployment holds significant room for improvement.Your Responsibilities:Develop and enhance the core inference engine that drives ComfyUI.Optimize large models for speed and memory efficiency.Collaborate with our core team to architect new features.Tackle complex technical challenges within the visual AI domain.Contribute to the future direction of our technology.Experience with diffusion or LLM models, as well as creating custom nodes for ComfyUI, is highly beneficial.