Machine Learning Jobs in Sunnyvale, CA

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.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

Design, implement, and deploy scalable ML training and evaluation platforms. Own end-to-end projects, make architectural decisions, drive prioritization, collaborate cross-functionally, and mentor engineers to accelerate autonomous driving model development.

Build and deploy scalable ML training and evaluation platforms, drive technical projects and architectural decisions, collaborate with partner teams, prioritize platform improvements, ensure reliability and performance, and mentor junior engineers.

The role involves optimizing large-scale ML infrastructure for autonomous vehicles, conducting analyses, collaborating with teams, and improving system performance.

Join the Inference team to implement features and improvements for model serving on a GPU platform, focusing on performance and reliability.

Senior individual contributor designing and deploying onboard ML solutions for autonomous driving. Develop models (detection, tracking, trajectory, safe AI), build training pipelines and deployment tooling, translate research into production, collaborate cross-functionally, and mentor engineers.

Design, implement, and deploy ML models for autonomous driving (detection, tracking, trajectory generation). Apply advanced techniques (imitation learning, RL, foundation models), collaborate cross-functionally, and support production ML systems and model deployment.

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.

Build large-scale machine learning systems for advertising, including ranking, recommendation, targeting, prediction, optimization, and creative generation. Develop feedback loops connecting generative AI outputs to advertising performance signals, apply prompt engineering and post-training methods, run experiments, and ship reliable production ML systems. Collaborate across Product, Research, Engineering, and go-to-market teams to deliver AI-native advertising products.

Build and scale Waymo’s ride-hailing marketplace systems for trip management, vehicle-rider matching, pricing, and vehicle positioning. Develop infrastructure to train and deploy machine learning and optimization models for real-time decisions. Create experimentation, monitoring, and analysis systems for deployed models, and partner with data scientists to productionize pricing and matching models.

Design and define architectures for complex embedded SoCs, translating system requirements into optimized chip requirements and custom logic. Research and evaluate vendor IP, maintain architecture documentation, participate in design and test-plan reviews, define coverage and performance targets, and support silicon bring-up, system validation, debugging, and implementation. The role collaborates across systems, software, design, and verification teams.

Conduct research on deep multimodal perception models for autonomous driving. Responsibilities include analyzing model representations, developing active learning and data curation algorithms, creating automated VLM labeling workflows, training and evaluating neural networks, and validating performance using large-scale vehicle sensor datasets and simulation environments.

Leads the design and development of large-scale search and discovery relevance systems, including machine learning infrastructure, serving, pipelines, and AI products. Drives technical strategy, execution, operational excellence, and business impact while partnering with senior stakeholders. Explores deep learning and large language model innovations, improves search quality, resolves complex technical challenges, and recruits and develops high-performing engineering talent.

The Research Engineer will develop and deploy machine learning applications focused on severe weather, collaborating on system processes, and maintaining production environments.

Design and implement AI-driven optimizations for software development workflows, leveraging LLMs and Agentic AI to enhance productivity and reliability.

Ph.D. interns apply machine learning and AI to semiconductor chip design or enterprise engineering tools. Responsibilities include developing ML models for EDA automation, placement, timing, power, and verification, or deploying LLM, RAG, and agentic systems. Interns will train, evaluate, and deploy models, conduct rigorous experiments, collaborate with engineering and technology stakeholders, assess production performance and safety, and present findings to leadership. The role requires doctoral research in AI, machine learning, computer science, electrical engineering, data science, or a related field.

As a Staff Machine Learning Engineer, you will lead the development and deployment of AI models for autonomous driving, focusing on safety and reliability. Responsibilities include driving the roadmap, training models, and collaborating across teams.

Lead reinforcement learning development for end-to-end autonomous driving models. Responsibilities include designing offline and off-policy RL methods, improving reward models, building large-scale training and evaluation workflows, diagnosing distribution shift and optimization failures, validating policies through simulation and on-road testing, productionizing successful methods, and mentoring engineers. The role also leads development of learned emergency trajectory models for safety-critical maneuvers such as evasive steering and emergency braking.

Define long-term architecture and technical strategy for Target's enterprise ML/Ops platform. Design cloud-native, scalable, secure Kubernetes-based platforms; establish ML lifecycle, deployment, governance, observability, and responsible AI standards; evaluate technologies; mentor engineers; and partner with infrastructure, security, and product teams to modernize platform capabilities.

Lead data science for Search & Discovery: analyze customer search interactions, design and measure experiments, build datasets and analytics pipelines, and optimize ML/LLM ranking models to improve relevance and personalization.

Develop and optimize real-time computer vision and ML systems for detecting, tracking, and classifying drones on an autonomous gun turret. Implement resource-efficient models, integrate vision systems with hardware, and validate performance across diverse, challenging environments to harden prototypes to military-grade standards.

Develop and optimize real-time computer vision algorithms and ML models for drone detection, tracking, and classification; integrate CV systems with embedded hardware and sensors; test and harden systems for military-grade, safety-critical deployment.

Join Hiring.Cafe as a Founding Machine Learning/AI Search Engineer to develop AI recommender systems and scalable infrastructure, contributing to a transformative job search engine.

Research and implement core algorithms for a self-improving reasoning engine. Develop data-efficient methods to probe LLMs, extract hidden knowledge, synthesize fragments into reliable answers, and design autonomous self-improvement loops. Publish and bring novel ideas to production.