Machine Learning Jobs in Sunnyvale, CA

The ML Perception Software Engineer will develop perception modules for autonomous vehicles, focusing on machine learning and computer vision to enhance navigation capabilities.

Conduct research on autonomy and simulation, publish findings, collaborate with teams to deploy algorithms. Focus on scalable autonomy using large-scale data.

Lead a team of data scientists to enhance infrastructure through data science, focusing on capacity planning, performance optimization, and customer experience.

Develop and maintain performance models of hardware, while collaborating with architecture and runtime teams. Optimize performance and contribute to architectural reviews.

The role involves improving demand forecast accuracy, implementing ML models, and collaborating with various teams to analyze market trends.

Lead vision, strategy, and execution for next-generation content intelligence products. Drive zero-to-one discovery, prototyping, and experiments; define reusable intelligence systems (knowledge, ranking, agents); partner cross-functionally to integrate into Edge, Windows, MSN, and Copilot; set success criteria and transition proven experiences to scale; mentor PMs and influence senior leaders.

Lead causal measurement and analytics for media investments: design quasi-experimental and observational approaches, apply interpretable ML and statistics, write reproducible Python/SQL analysis, surface actionable insights for marketing partners, and uphold data ethics and stewardship.

Design, develop, debug, and document complex software for semiconductor equipment components and devices. Create test procedures, specifications, and test plans for simulators, test benches, and lab environments. Troubleshoot software issues, implement fixes, analyze requirements, coordinate schedules, and communicate with internal and external customers. The role applies software engineering, machine learning, and AI concepts to semiconductor and display technology solutions.

Design, implement, maintain, and test high-precision vehicle localization software for Aurora’s autonomous driving fleet. Develop pose estimation solutions using high-rate sensor data, verify system safety and efficiency, and collaborate with engineers and stakeholders across product teams. The role requires expertise in C++, Python, state estimation, sensor modeling, spatial transforms, linear algebra, and probabilistic estimation, with robotics, autonomous vehicle, machine learning, or GPU experience preferred.

Develop production-grade perception and machine learning systems for autonomous driving, including object tracking, sensor fusion, action and attribute recognition, and semantic understanding. Research deep learning models, analyze failures, optimize large-scale training pipelines, and deploy real-time solutions on Aurora’s Driver platform. The role also involves architectural strategy, software lifecycle ownership, and cross-functional collaboration across simulated and on-road testing.

Lead development of online mapping perception algorithms for Aurora’s autonomous driving system. Research and build deep learning models, develop verification and validation techniques, and collaborate with mapping, simulation, and safety teams. Build infrastructure and tests supporting deployment decisions, then integrate, test, and deploy production-ready solutions into the Aurora Driver codebase.

Develop real-time behavior-planning algorithms for autonomous vehicles, selecting safe and comfortable trajectories. Integrate motion planning with machine learning, build production-grade Aurora Driver software, and create metrics and testing workflows for algorithm iteration. The role requires strong Python or C++ programming experience in low-latency or real-time environments and experience productionizing motion-planning algorithms. GPU and machine-learning toolkit expertise is preferred.

Develop and optimize deep learning and machine learning perception models for autonomous vehicles. Address object detection, traffic-actor tracking, action recognition, and semantic scene understanding. Deploy production-ready software to real-time vehicle platforms, build scalable ML pipelines, analyze large datasets, and resolve failures through on-road and simulated testing. Collaborate with cross-functional engineering teams to deliver robust computer vision systems using 3D sensor data from LiDAR, Radar, and cameras.

Lead a team to measure and improve enterprise search relevance and ranking using ML and LLM/RAG techniques. Manage end-to-end model development, evaluation, deployment, cross-functional collaboration, and team growth to ensure scalable, production-grade search quality.

Build, optimize, and scale ML infrastructure for training, evaluating, and serving large language models. Develop abstractions, productionize pipelines and microservices, improve LLM latency and monitoring, and collaborate cross-functionally to support enterprise ML systems at scale.

Scale and optimize a generative, agentic conversation engine for enterprise use: build product infrastructure and neutral APIs for chat clients, minimize latency and memory, enable private-domain knowledge and multilingual translation, implement logging/tracing/metrics, provide debugging tools, and collaborate with ML, product, and support teams.

Own DataVisor’s product marketing, PR, and analyst relations programs. Develop positioning, messaging, thought leadership, product assets, campaign materials, and executive communications while translating complex fraud, AML, AI, and SaaS technologies into customer-focused narratives. Collaborate across Product, Sales, Customer Success, Marketing, and leadership; manage agencies and analyst engagements; support launches and competitive intelligence; and use AI tools and agentic workflows to improve marketing productivity.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Design, develop, and deploy enterprise agentic AI applications using LLMs, RAG, AI agents, and orchestration frameworks. Build scalable AI services, models, APIs, and integrations with PLM, SAP, and other enterprise systems supporting engineering and manufacturing workflows. Collaborate with business and IT teams throughout the SDLC, improving solution quality through prompt engineering, evaluation, optimization, and production support.

Lead the Platform Software Engineering team building and scaling core data infrastructure for research, market-making, and risk platforms. Hands-on role combining individual contributor work, system architecture, cross-functional project leadership, and people management while collaborating with Research and Business Operations.

Design, develop, and deploy production-level vehicle control software (ISO 26262) for L4 autonomy. Translate planned trajectories into safe, precise motions, integrate controls with vehicle platforms, collaborate across hardware and autonomy teams, and explore ML methods to improve dynamics, drivability, and platform bring-up.

The Forward Deployed Research Scientist engages directly with AI labs to develop data strategies, fine-tune models, run experiments, and collaborate on publishing research, all in a fast-paced client-focused setting.

Lead individual-contributor responsible for designing, coding, and deploying deterministic and stochastic supply network simulations and AI/ML augmentations to forecast long-lead procurement. Audit large datasets, translate outputs into VP-level recommendations, modernize legacy planning into automated high-performance scripts, and partner with supply chain, procurement, finance, and engineering to optimize sourcing and material forecasting.

Build and maintain backend APIs and data pipelines that ingest and process large-scale construction data (360° video, schedules, IoT). Optimize storage and query performance using DuckDB and PostgreSQL, extend Airflow workflows, integrate Wasm for local computation, monitor data quality, and collaborate with frontend, CV, and ML teams to deploy production models and analytics.

Source and screen passive technical talent across AI, machine learning, robotics, computer vision, SoC, digital health, and data science roles, including Director-level executives. Develop multichannel sourcing strategies, analyze market data, advise recruiters and hiring managers, build diverse talent networks, promote employer branding, and track recruiting conversion metrics.