Machine Learning Jobs in Oakland, CA

The role involves supporting product requests from definition to integration, streamlining the model development process, and maintaining a scalable model stack.

Develop and train computer vision and multimodal models, design scalable AI frameworks, and improve systems through fine-tuning and reinforcement learning techniques while collaborating with multiple teams.

The Senior Machine Learning Engineer will lead product requests from problem definition to integration in the onboard driving system, focusing on audio perceptions for autonomous vehicles.

The Senior Machine Learning Engineer will lead the design and deployment of large-scale ML systems, focusing on generative AI, model optimization, and collaboration with cross-functional teams.

Develop multimodal large language models for autonomous vehicles, optimizing performance and safety through advanced machine learning techniques and real-world data validation.

The role involves optimizing 3D rendering of sensor data using GenAI/ML, collaborating with teams to enhance sensor simulation realism, and ensuring high fidelity in synthetic data generation.

Develop advanced multimodal large language models for autonomous driving, optimize solutions using real-world scenarios, and collaborate with engineering teams.

The role involves designing and developing scalable AI/ML systems, evaluating model performance, and collaborating with various stakeholders to accelerate drug discovery.

The role involves building RL environments to automate ML engineering, requiring expertise in ML and software engineering, coding in Python.

The Product Manager will define and drive the strategy for Plaid's data and ML ecosystem, focusing on infrastructure and governance to enhance product capabilities across teams.

The Senior Machine Learning Engineer will design, implement, and scale advanced foundation models, collaborate with researchers, and build AI systems. Requires hands-on experience with distributed training pipelines and proficiency in AI frameworks.

The Machine Learning Engineer II will develop and maintain AI/ML systems, manage cloud operations, and collaborate across teams to deploy ML-powered tools.

As a Senior Generative AI Engineer, you will lead the development of models for synthetic data generation and enhance the safety and capability of autonomous driving solutions at Zoox.

The AI/Machine Learning Engineer will design and optimize backend systems, integrate AI/ML frameworks, and collaborate on technical solutions in a dynamic environment.

The Technical Lead Manager will build and optimize Scale's ML training and inference platform, collaborating with ML teams to advance model and data curation technologies.

Join Thinking Machines Lab as a Research Scientist or ML Engineer to advance AI systems through collaborative research and scalable infrastructure.

Design and maintain models predicting ad conversion rates, oversee ad ranking systems, and communicate technical concepts to a broad audience.

Join PatternAI's data team as a Machine Learning Engineer, building ML pipelines and managing MLOps infrastructure for model optimization.

Develop and integrate AI agents for security platforms, focusing on natural language data processing and automating security tasks.

As a Research Engineer for ML Systems, you'll optimize GPU clusters for AI, develop advanced technologies for consumer AI models, and improve model training and inference performance.

The role involves supporting ML infrastructure, building diagnostic tools, managing deployments, and optimizing GPU utilization for ML projects.

The Director of Machine Learning will lead the development of ML models for audio, vision, and language applications, mentor the team, ensure project milestones are met, and collaborate with different stakeholders.

Design and maintain ML code for LLM safety, implement techniques for robust evaluation, and collaborate with product and engineering teams.

The role involves owning an LLM safety domain, generating synthetic data, training LLMs, conducting benchmarking, and developing techniques for safer models. Co-author research papers with the team.

Develop and optimize ML algorithms for fraud detection, risk modeling, and personalized experiences by leveraging large datasets and modern modeling techniques. Collaborate with engineering teams for deployment and monitoring.