Machine Learning Jobs in Sunnyvale, CA

Develop and optimize computer vision models for drones, improve ML pipelines, collaborate on R&D, and tackle advanced technical challenges.

Design and implement distributed cloud GPU training for ML, build end-to-end pipelines, collaborate on data problems, and uphold ML best practices.

Develop and apply deep learning and data analysis techniques to create scalable vehicle simulation models, collaborating with teams to advance autonomous technology.

Develop and lead evaluation systems, benchmarks, and large-scale data pipelines for Waymo foundation models and embodied AI agents. Evaluate models across pretraining, supervised fine-tuning, and reinforcement learning for quality, safety, and realism. Collaborate across teams to bring innovative machine learning technologies into production. The role requires strong ML engineering, deep learning, distributed systems, programming, analytical, and debugging skills.

Build, train, fine-tune, and evaluate computer vision and scene-understanding models for offline autonomous-driving measurement. Improve accuracy and generalization across vehicles, markets, and conditions using large models, temporal context, and multi-task learning. Define ground truth, benchmarks, metrics, and statistically defensible evidence for validation and safety cases. Collaborate across modeling, evaluation, data, and simulation teams while mentoring engineers and driving significant modeling workstreams.

Lead data-centric work for embodied AI: mine and curate fleet driving data, build large-scale enrichment and labeling pipelines, train and fine-tune foundation models, and design rigorous offline and closed-loop evaluation to improve safety and generalization across cities, vehicles, and sensor rigs.

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

Develop high-performance software for silicon test pattern generation, silicon application, failure analysis, and yield learning. Create parallel graph traversal and analysis methods, collaborate across technical teams, and apply LLMs, RAG, graph-based machine learning, and reinforcement learning to electronic design automation solutions. The role also involves large-scale model training, data processing, agentic applications, and advanced multi-agent and vector database systems.

Develop end-to-end perception solutions for autonomous driving, leveraging deep learning and data-driven approaches to enhance system accuracy and efficiency.

Lead development of 3D geometric vision and world foundation models for autonomous driving. Responsibilities include designing and training deep-learning models, building scalable sensor-data, auto-labeling, SLAM, reconstruction, evaluation, and ground-truth pipelines, and deploying models on production vehicles. The role requires technical direction across geometric vision, multimodal sensing, distributed training, neural rendering, 3D representations, and real-time inference.

Build and productionize large-scale ML systems for search, ranking, and personalization. Handle petabyte-scale data pipelines, design transformer/embedding-based retrieval and real-time model serving, track metrics, collaborate cross-functionally, and mentor engineers to improve relevance and customer discovery.

Lead an ML Platform engineering team building large-scale GPU cluster architecture, training and inference orchestration, and performance optimization. Own design, scaling, scheduling, networking, and resource management while partnering with research and stack teams to accelerate production deployment and drive hiring and mentorship.

The role involves building and managing data infrastructure and pipelines for machine learning, automating model retuning, and ensuring efficient data flow from collection to model training.

Build and deploy ML systems for search, recommendation, ranking, retrieval, matching, and personalization. Work on query understanding, embeddings, NLP/LLMs, end-to-end ML pipelines, experimentation, and monitoring to improve relevance, engagement, conversion, and marketplace efficiency in a consumer local-search product.

Develop and deploy state-of-the-art ML models for predicting interactive agent behavior and building controllable agents for closed-loop simulation. Conduct research in generative sequence modeling, transformers, diffusion models, and sequential decision-making, and collaborate with planning and autonomy teams to produce deployable solutions for real vehicles.

Design and deliver production-quality Metropolis AI solutions using Cosmos foundation models. Identify model limitations, generate synthetic data, refine tuning and architectures, partner with Cosmos and cross-functional teams, open-source research artifacts, and advance generative and simulation-based perception for physical AI deployments.

Research and build large-scale neural reconstruction and generative models to complete and synthesize photorealistic 3D spaces from sparse/noisy captures. Design and optimize training and inference pipelines for multi-view, point cloud, mesh, and video data, translate prototypes into production systems, define evaluation benchmarks, and collaborate with product and engineering teams to integrate models into Matterport's digital-twin platform. Contribute to publications, patents, and the strategic roadmap.

Develop and deploy perception ML models for autonomous vehicles (detection, sensor fusion, tracking, classification, intent). Own end-to-end lifecycle: POC, model iteration, onboard deployment, monitoring, and optimization for latency and scale.

The Performance Engineer - Inference will optimize model inference speed and throughput, debug low-level kernel performance, and develop tools to visualize performance data.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly detection, visualization and analysis tools, and frameworks for failure classification and regression detection. Extend compilers, runtimes, and instrumentation for advanced profiling. Improve bring-up and low-level debug workflows, partner cross-functionally across hardware, firmware, compiler and runtime teams, lead high-impact initiatives, and support incident response and long-term corrective actions.

Optimize and deploy ML models to production embedded runtimes. Improve inference latency and efficiency across accelerators, prune/quantize models for memory‑constrained platforms, profile on target hardware, and collaborate with ML and software teams on architecture and integration.

Build and operate end-to-end ML infrastructure: distributed cloud GPU training, datasets, training frameworks, evaluation, deployment, and integrate pipelines into product workflows while collaborating with modeling teams.

Integrate state-of-the-art machine learning (especially generative) into production-grade sensor simulation for LiDAR, Radar, and Camera. Collaborate with research, rendering, and physics teams to develop, evaluate, and deploy models and tooling that produce production value and customer impact.

Develop, prototype, evaluate, and deploy ML-first behavior prediction and planning models to forecast motion and interactions of road users. Build software and infrastructure for behavior-specific ML, collaborate with perception and planning teams, and productionize models for autonomy systems.

Responsible for designing and developing scalable data pipelines, storage systems, and monitoring tools for ML data infrastructure, ensuring system integrity and performance evaluation.