Machine Learning Jobs in Sunnyvale, CA

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.

Build and operate cloud-native, large-scale data pipelines and scalable services for the Mist platform. Develop AI-driven routing intelligence to detect, troubleshoot, predict, and mitigate network issues in multi-tenant data centers, collaborating across teams and leveraging AI-assisted development tools.

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.

Build and consolidate data infrastructure for ML training: design automated ETL/ELT pipelines, implement DataOps practices (validation, monitoring, anomaly detection), integrate annotation workflows, manage dataset versioning and storage, and collaborate with ML engineers and operations to produce high-quality, reproducible training datasets.

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.

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.

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.

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.

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.

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.

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.

As a Machine Learning Engineer, you will apply ML techniques for perception tasks, develop model training methods, and evaluate performance in autonomous driving applications.

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.

Develop machine learning models and infrastructure for assessing autonomous vehicle behavior. Collaborate across teams to optimize production systems and model performance.

Develop and enhance the data infrastructure for Waymo's ML models, focusing on data needs, storage efficiency, and integration across various use cases.

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

Responsible for architecting high-performance machine learning silicon, collaborating with software and design engineers, and driving innovation in AI hardware solutions.

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

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.

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.