Top Machine Learning Engineer Jobs in San Francisco, CA

Lead the development and implementation of machine learning solutions, managing a team to drive strategic decisions and innovative business outcomes.

As a Senior ML Engineer, you'll design, develop, and deploy ML models for product features, collaborate with teams, and drive research initiatives.

The Senior Applied AI/ML Scientist will lead the design and deployment of AI solutions, collaborate on impactful features, and enhance product capabilities by leveraging advanced AI and ML techniques.

The Machine Learning Engineer develops production ML algorithms and recommendation systems, collaborates with teams, and optimizes user experiences.

As a Senior Machine Learning Engineer, you will enhance diffusion models, conduct research, collaborate on system implementations, and communicate findings effectively.

The Machine Learning Engineer will build tools for interpretable AI, optimize infrastructure, and integrate workflows into products, ensuring reliability and performance.

Lead the ML research and engineering teams at Wispr, focusing on developing multimodal speech recognition models and RL-based personalization of LLMs. Oversee model training, serving, and team growth.

As a Machine Learning Research Intern, you will develop and evaluate prediction models for biological impact using large-scale datasets, collaborating closely with the ML team.

The ML Engineer will develop applications for biomarker and target discovery, supporting scientists with multi-step workflows and analyses in drug development.

As a Staff ML Engineer, you'll lead complex model quality improvements in clinical AI, spanning research and engineering, optimizing performance under high compliance conditions.

As a Senior Machine Learning Research Engineer, you'll build and optimize large-scale ML training systems, focusing on distributed infrastructures and performance enhancements for innovative model training.

The role involves developing and optimizing scalable AI/ML systems, collaborating across teams, and enhancing workflows to support drug discovery.

Develop innovative machine learning algorithms for modeling proteins, collaborate with biologists, and refine predictions through experiments.

The role involves building ML systems, improving models across product surfaces, and leveraging data for personalization and user engagement.

Develop and improve machine learning models for Pinterest's various product surfaces, utilizing deep learning and advanced data processing.

Develop and execute ML technology stack vision for Ads, improve ML models, and enhance content understanding using LLMs in a collaborative environment.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

As a Scientist in Machine Learning, you will develop novel models for RNA structure and drug targeting, advancing the R&D platform and collaborating with interdisciplinary teams.

Responsible for designing and implementing predictive models for monetization and learner engagement, integrating machine learning systems into existing products, and collaborating with cross-functional teams to deliver measurable business outcomes.

Design, implement, and deploy machine learning models and systems for recommendations and related problems. Collaborate with engineers, data scientists, and product partners to analyze data, build proofs-of-concept, develop ML/optimization models, write production-quality code, and evaluate models against business goals.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

The role involves building and improving systems for training AI models, focusing on algorithms and infrastructure for reinforcement learning. Responsibilities include enhancing performance, reliability, and usability of systems to support research.

Conduct applied research in robotics and develop ML pipelines for training models on data collected by Scale, collaborating with customers and cross-functional teams.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.

You will lead technical strategy and engineering for ML-powered messaging features at Airbnb, integrating solutions and collaborating with teams to enhance user communication. Responsibilities include system design, mentoring, and establishing engineering standards.