Top Machine Learning Jobs in San Francisco Bay Area, CA

Develop and deploy machine learning models for risk assessment, collaborating with teams to enhance Gusto's product offerings while ensuring model performance and reliability.

Design and deploy machine learning models to combat financial crimes while collaborating with stakeholders and maintaining compliance with regulations.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.

Develop and productionize ML models for assessing autonomous vehicle behavior, build ML infrastructure, and collaborate across teams for deployment.

The Senior Software Engineer will evaluate and launch machine learning models for mapping, build backend infrastructure, and manage data processing on a large scale.

Lead a team of machine learning engineers to improve infrastructure efficiency through ML and optimization algorithms at Databricks.

As a Senior Applied AI Engineer, you will develop and deploy machine learning models, improve system performance, and drive AI initiatives at Databricks.

Lead centralized Machine Learning efforts at Affirm, managing model development lifecycles, fostering AI adoption, and overseeing a team of ML professionals to enhance business outcomes.

As a Senior Machine Learning Engineer, you will develop machine learning models, manage their lifecycle, build infrastructure, and collaborate with teams to enhance our capabilities in various ML areas.

Train personalized ML models, create synthetic data pipelines, evaluate model quality, and develop safeguards for AI privacy and misuse.

As a Machine Learning Engineer, you will design and deploy advanced models for clinical reasoning, manage ML systems, and collaborate with teams to enhance healthcare delivery.

As a Senior Machine Learning Engineer, you will design and enhance the AI evaluation platform, focusing on data pipelines and model benchmarking systems, collaborating closely with team members.

The Machine Learning Engineer role at Patreon involves developing machine learning models, analyzing data, collaborating across teams, and deploying solutions to enhance creator support.

In this role, you'll develop machine learning models for logistics optimization, retailer underwriting, and growth strategies, collaborating with a skilled team to enhance the marketplace experience.

As a Distinguished Machine Learning Engineer, you'll lead teams in developing and implementing scalable ML applications, mentor engineers, and drive innovation in the financial sector.

Lead the development of ranking and relevance models, design ML systems for patient-provider matching, and mentor junior team members.

Design and build ML models, optimize data pipelines, lead teams, and support AI software components, while advancing AI capabilities for improved customer experiences.

As a Staff Machine Learning Engineer, you'll lead AI product development, leveraging LLMs and embeddings, collaborating on technical strategy and coding performance enhancements.

As a Machine Learning Systems Engineer, you will build and scale AI infrastructure, leading projects and mentoring junior engineers, while focusing on the ML project lifecycle and system architecture.

As a Machine Learning Engineer, you'll develop and ship models for an AI platform, own projects from start to finish, and work closely with customers to enhance product features.

Lead the development of machine learning systems for fraud detection and risk modeling at scale, collaborating across teams and influencing ML strategy.

Develop mathematical models for discounting systems, optimize algorithms, collaborate with cross-functional teams, and drive data-driven decision-making.

As a Senior Machine Learning Engineer, you'll design and deploy ML models to prevent fraud, enhance safety, and support financial services at Block.

The role involves developing an AI agent platform, building ML models, and enhancing user experiences with advanced algorithms. Collaboration and fast-paced execution are key.

Design and maintain ML pipelines and data pipelines, optimize fraud detection models, collaborate with teams, and ensure data quality for high-scale applications.