Machine Learning Jobs in Sunnyvale, CA

Optimize large-scale ML training and inference workloads by profiling bottlenecks, implementing efficiency improvements (parallelism, mixed precision, compilation), building observability and benchmarking tools, and collaborating with research and platform teams to scale models and increase training throughput.

Design and optimize large-scale continual pre-training pipelines for vision-language foundation models; research novel pre-training techniques integrating multimodal sensor data; develop evaluation metrics for model performance and transferability in autonomous driving; and productionize advances from LLM/VLM research.

Own and scale ML tasks for multi-modal perception in autonomous vehicles, analyze real-world system behavior, monitor production models, develop automated detection and debugging tools, and maintain safety-critical metrics and longtail issue tracking.

Develop scalable web applications using React and Next.js, implement backend solutions in Python, maintain DevOps practices, and collaborate with AI/ML teams to deploy applications.

Lead architecture and implementation of scalable ML data pipelines and tooling to turn autonomy data into training datasets. Build data selection, active learning, inference-at-scale, vector DB management, labeling integration, QC automation, and introspection tools while partnering with ML researchers and infra teams.

Perform original research on LLM training/evaluation, RAG, RL, and AI agents; design, implement, and evaluate new models and methods; collaborate with researchers; and publish and present results at top ML/NLP conferences while influencing product roadmap.

The Machine Learning Engineer will research and develop AI-driven solutions to analyze customer data, optimize ML models, and collaborate with cross-functional teams.

Lead collaboration to improve ML workloads on cloud and self-driving cars, optimize models, and mentor junior engineers.

Analyze workloads for hardware mapping, design machine learning solutions, and collaborate with cross-functional teams on high-performance architecture.

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 role involves applying machine learning techniques to circuit design, analyzing datasets, building and optimizing models, and collaborating with teams on various projects in VLSI and circuit design.

Develop and maintain machine learning models for robotics applications, collaborate across teams, and contribute to innovative solutions in AI and mobility.

Fine-tune and optimize LLMs (including multi-modal) for humanoid robots and autonomous vehicles; build scalable training and deployment pipelines; work on model efficiency (quantization, weight sharing), synthetic data generation, and cross-team product alignment.

The role focuses on measuring and improving the quality of AI assistants through evaluation datasets, large-scale evaluation pipelines, and observability infrastructure, requiring collaboration with engineers to ensure quality.

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

Develop, train, and optimize deep learning models for autonomous trucks (detection, tracking, mapping, end-to-end planning). Execute full ML lifecycle from data curation to deployment, collaborate with simulation/infrastructure/planning teams, and evaluate SOTA computer vision and generative AI methods to address real-world corner cases.

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

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

Lead the development of RL training frameworks and data processing pipelines for AV models, ensuring quality and scaling training workflows.

The Software Engineer will develop infrastructure for ML models, optimizing the economic engine for ride-hailing services and managing backend platforms for real-time decision-making in pricing and vehicle matching.

Lead research and production deployment of ML and computer vision systems for 3D capture and reconstruction. Improve camera pose estimation, mesh/surface reconstruction, texturing, semantic understanding, and real-time capture guidance. Apply deep learning, multi-view geometry, and neural rendering techniques; collaborate across hardware, software, and product teams; and manage the full ML lifecycle from data collection to production monitoring. Write high-quality Python and C++ code.

The Staff Machine Learning R&D Engineer will innovate machine learning algorithms for spatial computing, collaborate with teams, and translate research into production solutions.

Lead initiatives to enhance the ML platform, mentor junior engineers, build scalable ML models, and ensure code quality while driving innovation in a startup environment.

As a Machine Learning Engineer, you will design core platforms for autonomous agents, engineer data systems, build tools, and enhance product experience while collaborating with data scientists to deploy advanced models.

Join an AI residency to prototype ML and AI solutions for business process optimization, build data pipelines and large-scale analyses, collaborate cross-functionally, evaluate project value chains and risks, and engage with the resident community while advancing research on agentic systems and LLM-based workflows.