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.

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 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.

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.

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.

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.

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

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.

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.

Lead design, build, and operate AIOps and observability platforms (metrics, logs, traces, alerts, dashboards). Define roadmap and standards, collaborate with teams, mentor engineers, and implement ML/LLM-based anomaly detection, forecasting, root-cause analysis, and agentic debugging across large-scale cloud, on-prem, and bare-metal environments.

Lead R&D for underwater acoustics: define scientific vision, develop detection/classification/localization and acoustic mapping capabilities, guide sensing design and deployments, run field experiments and analyses, translate research into operational products, and represent the company to partners and customers.

Lead development of Sofars underwater acoustics platform: design detection, classification, localization, and acoustic mapping methods; run field experiments and simulations; translate research into operational products; collaborate across engineering and science teams; represent Sofar externally.

Lead modeling of weather patterns and their impact on the Waymo Driver. Build pipelines and spatio-temporal models integrating multiple weather data sources. Measure performance in adverse weather, develop scalable analysis frameworks, optimize operational mitigation processes, and collaborate with product and engineering. Act as a technical contributor and lead, scope ambiguous problems, and champion measurement rigor across teams.

Define and position datacenter GPU products for AI, ML, and HPC workloads (especially LLMs). Identify customer needs, translate into product value propositions, lead cross-functional go-to-market planning, and create sales collateral while coordinating with engineering, operations, and field teams.

Support compact device modeling for advanced Intel process technologies (FinFET/GAA). Develop and validate transistor compact models (BSIM-CMG), use extraction tools and simulators, analyze data with Python, apply ML for model development, collaborate across teams, and benchmark device and circuit performance including layout-dependent and self-heating effects.

Develop, test, and document embedded software and firmware for an SoC-based sensor/BLE board. Implement multi-channel data acquisition, optimize signal-processing and proprietary algorithms, create BLE/server communications protocols, build device drivers, perform testing, and produce documentation.

This role oversees cross-organizational programs, ensuring operational resilience during market expansions, and driving program outcomes through effective management and deployment strategies.

Design and develop machine learning infrastructure, enhancing core components for model training, monitoring, and serving. Collaborate with ML engineers on robust pipelines.

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.

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.

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

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

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.

Lead and scale machine learning efforts, manage teams, drive operational excellence, and align ML strategies with business goals.