Machine Learning Jobs in Sunnyvale, CA

The Founding ML Research Engineer will design and evaluate ML models, develop experimentation pipelines, and contribute to AI research while optimizing training processes.

As a Founding ML Engineer, you'll develop ML models for lead optimization, automate demand generation processes, build data pipelines, and collaborate with teams to enhance growth through AI solutions.

The Staff Machine Learning Engineer will design and develop machine learning systems for ad targeting and relevance at Coupang, innovating workflows and mentoring teams.

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.

Build and productionize large-scale data pipelines and platforms to generate ML and evaluation datasets from driving logs and simulation data. Design tools for data mining, exploration, and analysis, and collaborate with Data Science and Quantitative Analytics to deliver high-impact, production-ready infrastructure and dataset generation.

As a Senior Machine Learning Engineer, you'll design and implement large-scale E2E driving models, enhance data workflows, and collaborate with cross-functional teams to advance autonomous driving technologies.

Collaborate to develop generative models for autonomous driving, focusing on plan generation and optimizing models through reinforcement learning.

Develop and optimize generative models for autonomous planning using reinforcement learning and collaboration across teams. Requires strong programming skills and research experience in generative models.

As an Engineering Manager for Inference ML Runtime, you will lead a team to design, scale, and optimize AI inference systems on Cerebras hardware, ensuring high performance and overseeing execution of ML engineering initiatives.

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

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.

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.

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.

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.

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.

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

As an AI/ML Engineering Intern, you'll assist in building data pipelines, optimizing distributed systems, and deploying machine learning applications, gaining hands-on experience with advanced AI technologies.

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.

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.

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.

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

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.