Machine Learning Jobs in Oakland, CA

As a Staff ML Ops Engineer, you'll architect high-performance systems for AI/ML, manage Kubernetes infrastructure, and develop scalable backend APIs, enabling scientific discoveries across the materials science industry.

Design and deploy machine learning systems for autonomous vehicles, collaborating with teams to improve model performance and safety using real-world data.

The role involves developing ML systems for enterprise workflows, focusing on user understanding, task prediction, and optimizing ML pipelines. Key responsibilities include designing scalable evaluation frameworks and collaborating with teams to deliver production-ready solutions.

Develop and deploy ML-based perception algorithms for robotics, optimize pipelines, and collaborate across disciplines for integration.

The role involves designing and implementing machine learning systems for knowledge representation and temporal reasoning, focusing on organizational intelligence. Responsibilities include building LLM-powered pipelines, developing memory algorithms, and researching various ML techniques.

The ML Engineer will develop and enhance machine learning models for credit decisioning, drive technical innovation, and collaborate across teams to deliver impactful solutions for small businesses.

You will build a search and assistant product, improve personalization, train models, and write robust code while mentoring junior engineers.

The role involves solving customer problems by building ML solutions, engaging with various stakeholders, and taking ownership of the outcome through deployment.

The role involves building production ML models for voice AI, focusing on fine-tuning, deploying, and evaluating models to enhance human-like interactions.

As a Machine Learning Engineer, develop ML models, data pipelines, conduct analytics, and work on image and video inference for fish health and behavior.

As a Sales Executive, you will hunt for new clients, manage enterprise accounts, and create revenue streams while collaborating with internal teams.

Design and build agentic AI workflows for biological and clinical data curation and analysis. Implement context engineering (RAG variants), prompt and agent architecture optimization, LLM tool-use integrations, and evaluation benchmarks. Collaborate with bioinformatics and clinical experts to translate domain problems into reliable, production-quality agentic systems.

The AI/ML Engineer will design and deploy advanced AI models, optimize performance, and directly engage with government officials to transform industries through innovative AI solutions.

Design and implement machine learning algorithms for robotic manipulation, focusing on RL and IL. Collaborate across disciplines to ensure integration and successful deployment.

This internship involves developing machine learning approaches for biological data, designing experiments, collaborating on research, and presenting findings.

Develop and deploy manufacturing intelligence systems and machine learning models to enhance operational efficiency in production environments, collaborating across teams to achieve impactful solutions.

The ML Platform/MLOps Engineer will build and maintain infrastructure for large-scale ML systems, ensuring efficiency, reliability, and security while supporting the research team from experiments to production models.

Responsible for designing experiments to evaluate AI models, developing evaluation methodologies, analyzing performance data, and collaborating on production insights.

The Machine Learning Scientist will lead open-source research, design experiments, develop novel methodologies, and analyze large-scale data to advance AI model evaluation and transparency.

Design and develop scalable, high-performance data and API infrastructure for real-time processing. Mentor engineers and collaborate with teams to enhance AI model evaluations.

Lead development of scalable molecular dynamics pipelines, integrating physics-based models with machine learning frameworks to enhance molecular engineering and experimentations.

The ML Research Scientist will design Bayesian optimization frameworks, collaborate with lab scientists, and integrate ML models with experimental data.

The role involves designing de novo frameworks for molecular engineering, collaborating across teams to optimize data-driven biological experiments and integrating ML models with experimental systems.

Develop Cloud Data Infrastructure for Assured AI in autonomous vehicles, focusing on ML model training, evaluation, and deployment.

Develop deep learning models for autonomy applications, implement ML algorithms for self-driving vehicles, and resolve real-world testing issues.