Machine Learning Jobs in Oakland, CA

Lead and execute AI & ML projects, engage with stakeholders, mentor junior scientists, and develop advanced ML models while aligning with business goals.

The Machine Learning Engineer will develop LLM-driven systems for information extraction and workflow automation, enhancing conversational AI technologies for insurance tasks.

Design and build language models for AI security products, managing data pipelines, experiments, and model evaluations to ensure reliability and safety.

Lead the team responsible for the ML serving and deployment infrastructure at Pinterest, focusing on performance and productivity improvements while coaching team members.

As a Senior/Principal Machine Learning Engineer, you will build ML systems for voice moderation, mentor engineers, and shape technical direction.

The ML Research Engineer will optimize training frameworks, post-train models, collaborate on AI development, and create algorithms for agent training.

The Staff Data Scientist will design and deploy machine learning models to detect and prevent fraud, working with cross-functional teams to enhance risk scoring and improve fraud detection methods.

Lead machine learning efforts to enhance customer onboarding and product adoption through data analysis, model development, and cross-functional collaboration.

The role involves developing ML models for search and recommendation systems, managing AI technologies, and enhancing user experiences across Adobe products, requiring a strong background in AI, ML, and extensive industry experience.

As an Applied Machine Learning Engineer, develop, deploy, and optimize machine learning models while collaborating with cross-functional teams to enhance customer solutions.

Lead machine learning projects by defining the ML system development life cycle, mentoring teams, and integrating models for urban perception tasks.

As a Research Engineer, you will collaborate on advancing AI systems through reinforcement learning, while contributing to both research and engineering tasks.

The Machine Learning Engineer will manage the end-to-end ML lifecycle, collaborate with cross-functional teams, develop production models, and monitor their performance in high-stakes environments.

As an ML Engineer, you will design and develop ML solutions, build infrastructure for testing, training, and deploying models, and contribute to enhancing coding agent capabilities at Sourcegraph.

Eluvio seeks an AI-Machine Learning Intern to build ML models, collaborate on data science solutions, and enhance video/audio content intelligence.

Interns will collaborate with the machine learning and data science team to build models for video/audio content using large datasets and provide actionable insights.

The role involves researching, implementing, and optimizing ML approaches for 3D simulation, collaborating with teams, and building scalable cloud pipelines.

Design and implement machine learning models and systems for programmatic advertising, maintaining data pipelines and collaborating on product roadmaps.

Drive AI/ML-powered personalized experiences on Pinterest by defining product vision, collaborating across teams, and launching new features to enhance user engagement.

The Staff Data Scientist will lead the design and development of Game Understanding Agents using machine learning methods, mentoring engineers and setting technical direction.

The role entails developing AI/ML applications, collaborating across teams, optimizing code, and mentoring juniors while focusing on user experience.

Lead the design and optimization of infrastructure for ML systems, manage GitOps repository, Kubernetes environments, and observability platform, ensuring performance and security in production.

As a Machine Learning Engineer at Cognitiv, you will develop and scale ML infrastructure, optimize workflows, and collaborate with teams to enhance advertising solutions using deep learning.

Design and implement large-scale recommendation systems. Develop deep learning models for ranking and personalization and translate research into impactful production systems.

Design, build, and improve ML systems and infrastructure, mentor junior engineers, and write maintainable code in a customer-focused environment.