Top Machine Learning Jobs

Design and deploy production-grade ML systems, integrate models into software pipelines, and develop features leveraging ML for optimization and prediction problems.

As a Machine Learning Engineer in the Buyer Growth team, you'll design and deploy ML models to drive user acquisition and improve retention. You'll manage the entire model lifecycle and collaborate across teams to identify impactful growth opportunities.

Lead end-to-end machine learning solutions for autonomous driving, mentor engineers, design advanced models, and ensure safety and reliability in diverse scenarios.

The Staff ML Engineer will design and implement backend components for the ML Inference Platform, ensuring efficient model serving and collaborating with ML teams to enhance AI infrastructure at GM.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

Lead research on machine learning validation for robotics and autonomy systems, focusing on simulation, uncertainty modeling, and creating functional prototypes for testing.

The Senior AI/ML Engineer will enhance developer productivity by improving Python workflows, leading projects, mentoring engineers, and driving best practices.

Lead and develop advanced machine learning models for autonomous driving, collaborating with teams, and driving technical initiatives in a hybrid work environment.

Senior individual contributor building and maintaining underwriting and credit decisioning ML systems for Cash App Borrow and Afterpay. Responsibilities include feature engineering, model training, calibration, experimentation, deployment, monitoring, and portfolio-level analysis. Collaborate with cross-functional teams to align models with business and regulatory goals and develop AI-native engineering workflows and governance for reliable, auditable model development.

The Postdoctoral Researcher at Zhou Lab will develop machine learning models for biological data, specifically in genomics and gene regulation, collaborate with experimental biologists, and lead research projects for significant scientific discoveries.

As an Applied Scientist, you will design and implement machine learning models, develop personalized systems, and enhance decision-making through AI technology to improve user experience and business operations.

Develop and improve machine learning models and systems for clinical outcomes. Lead ML initiatives, mentor teams, and establish best practices in AI. Collaborate across engineering, product, and design for data-driven solutions.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

Design and deploy analytics and ML solutions: build data platforms, develop predictive models and pipelines, analyze structured/unstructured data, create visualizations and reports, collaborate with business partners, validate and document algorithms, and support large-scale data science initiatives.

Build, deploy, and monitor ML/AI models to automate processes and generate insights. Collaborate with stakeholders, Data Engineers, and Product Managers to design approaches, train and compare models, analyze data distributions, detect adversarial trends, and deliver production-ready solutions aligned to business KPIs.

Build and deploy predictive models and statistical analyses on large healthcare and retail datasets. Design ETL and data models, extract and manipulate multi-source data, develop algorithms, and support deployment of ML solutions across cloud environments and business use cases.

Lead the development of production-grade ML systems, aligning technical roadmaps with company priorities, mentoring ML engineers, and ensuring ethical use of ML/AI.

The Machine Learning Scientist will research, develop, and deploy machine learning solutions for real-time healthcare decision systems focusing on maternal health. Responsibilities include evaluating methodologies, collaborating with teams, and achieving product development goals.

The Staff Machine Learning Engineer will lead ML systems development for personalization and ranking, design architectures, and mentor team members, ensuring reliable operations at scale.

The Principal Machine Learning Engineer will design and scale ML systems for personalization and search, mentor engineers, and lead major ML initiatives at Fetch.

The role involves designing and scaling ML systems for prediction and recommendation, mentoring junior engineers, and leading projects.

Research and develop improvements to model personalization and agentic capabilities via RL and post-training methods. Own a research agenda, build robust evaluations, implement and debug code across the research stack, and collaborate with research and product teams to integrate advances into products.

Lead design, development, and production deployment of ML/DL models for targeting in a regulated biotech environment. Build scalable feature pipelines, prepare large complex datasets, ensure production-grade pipelines and monitoring, collaborate with cross-functional teams, and communicate model results to business stakeholders.

Lead architecture and roadmap for large-scale ML platforms powering search, ranking, and recommendations. Build and deploy distributed, real-time ML systems, translate research into production, advance recommender and generative AI capabilities, mentor engineers, and drive cross-functional ML initiatives aligned with business goals.