Machine Learning Jobs in Sunnyvale, CA

Lead the design, implementation, and deployment of scalable ML infrastructure for autonomous driving. Drive technical projects, mentor others, and collaborate with teams to enhance ML workflows.

Own and advance a power estimation and analysis platform: develop scalable tools and models to predict power-performance behavior, integrate telemetry and simulation data, correlate pre- and post-silicon measurements, automate visualization and reporting, and partner across architecture, firmware, validation, and silicon teams to guide architecture and product trade-offs.

Lead design, training, and evaluation of multi-modal ML perception models (camera, lidar, radar) for fallback autonomy. Define metrics, curate datasets, implement training and optimized inference pipelines for on‑vehicle constraints, and integrate models into production. Partner with safety, systems, and product teams for validation, verification, and deployment. Mentor engineers and promote ML and software engineering best practices.

Lead AI/ML safety engineering for Level 4 autonomous vehicles. Define SOTIF and functional safety strategies, derive requirements for learned systems, design runtime safety monitors and OOD detectors, assess dataset coverage and bias, analyze ML failure modes, and build evidence-backed safety cases. The role requires collaboration across perception, prediction, planning, systems, and safety engineering teams, with onsite work five days per week in Santa Clara, California.

Design, train, fine-tune, evaluate, and deploy production machine learning models for cybersecurity detection. Build efficient transformer and LLM systems optimized for latency, throughput, memory, cost, and reliability. Develop scalable inference infrastructure, data pipelines, and labeling workflows; collaborate with security researchers and product teams; monitor production models and improve detection quality and robustness.

Develop and deploy machine learning planning algorithms for autonomous trucks. Own the full ML lifecycle, including data curation, feature engineering, training, evaluation, deployment, monitoring, and improvement. Design trajectory and behavior-planning models, safety guardrails, validation pipelines, and performance metrics. Investigate failures and edge cases, optimize inference, and collaborate with perception, prediction, mapping, runtime, and systems teams to deliver reliable production systems.

Lead development of real-time machine learning and computer vision systems for robotic-assisted surgery. Design and deploy deep learning models for surgical scene understanding, segmentation, detection, 3D reasoning, and probabilistic inference. Own models from research through production integration with C++ and embedded robotic platforms, optimizing latency and reliability. Define clinically meaningful validation methods, collaborate with surgeons and engineering teams, mentor colleagues, and contribute to patents and regulated medical-device development.

Design, develop, and deploy reinforcement learning, behavior cloning, and imitation learning models for autonomous truck behavior planning. Own reinforcement learning pipelines from environment construction through training and modeling, contribute to distributed training infrastructure, analyze model performance and failure modes, and collaborate with simulation and planning teams to deploy models across driving scenarios.

Design and optimize production machine-learning runtime pipelines for autonomous vehicle motion planning. Build high-performance C++ inference, feature extraction, trajectory generation, and orchestration components for embedded platforms. Develop validation, safety guardrails, fallback mechanisms, testing, monitoring, debugging, and observability tools. Investigate complex runtime failures and optimize latency, throughput, memory, and multithreaded performance while collaborating with machine-learning and systems engineering teams.

Design and develop complex software solutions for semiconductor equipment, lead projects, define specifications, troubleshoot software problems, and communicate with customers.

The Technical Product Manager will lead product direction, interface with engineering teams, and ensure the successful deployment of AI-powered safety systems. Responsibilities include defining product vision, translating customer needs, and maintaining high product quality.

As a Research Engineer, you conduct pioneering research in autonomous systems and develop models using large-scale data and advanced simulators, collaborating with scientists and contributing to high-quality publications.

As a Research Engineer, you will conduct research in robotics and AI, work with hardware and software for robotic systems, and collaborate on research publications.

Build and operate production search and indexing systems at billion-vector scale for multimodal driving data. Develop embedding-based retrieval, metadata filtering, backend APIs, ingestion pipelines, and evaluation frameworks covering relevance, recall, latency, scalability, freshness, and cost. Own components from architecture through production operations, collaborate across ML and data teams, make build-versus-buy decisions, and mentor engineers.

Design and productionize machine learning solutions that predict and optimize GPU-accelerated Apache Spark workloads. Develop adaptive ML systems, AI agents, and optimization tools; collaborate with customers and partners on complex deployments; track advances in ML systems; and mentor engineers in data science and machine learning. The role requires expertise in large-scale data processing, modern ML/DL methods, LLMs, generative AI, reinforcement learning, online learning, feature engineering, and boosted-tree models.

Architect, train, and optimize multimodal deep learning models for autonomous vehicle perception. Build large-scale data mining, active learning, hard-example curation, and auto-labeling pipelines. Develop high-resolution vision and spatial-temporal transformer architectures, use foundation models for data curation and synthetic edge cases, and optimize models for onboard accelerator inference.

Develop, evaluate, and release learned driving policies for autonomous vehicles, particularly complex navigation and yielding scenarios. Bridge multimodal perception and planning outputs into safe behavioral actions, evaluate models in closed-loop simulation against safety metrics, and transition production-ready models into autonomous navigation systems. The role also advances data-driven policies, foundation-model experimentation, scenario generation, and end-to-end driving architectures.

Provide embedded legal counsel to product and engineering on privacy, IP, and consumer protection; lead Arena's global privacy program (GDPR, CCPA/CPRA, cross-border transfers); advise on AI governance and responsible deployment; support commercial contracts and data flow frameworks; build privacy processes, playbooks, internal agents, and legal-tech integrations to scale operations.

The Staff Technical Program Manager will manage simulation compute resources, develop execution strategies, and influence stakeholders for project success.

Lead design and architecture of sequencing-product software, applying pattern recognition and machine learning to large experimental DNA sequencing datasets. Collaborate with multidisciplinary scientists to advance parameter estimation algorithms, drive product development decisions, and mentor teams on algorithmic and software engineering work.

Develop and optimize high-volume Teradata data warehouse applications: design/architect solutions, model data, write and tune SQL, use Python scripting; ML and Hadoop experience a plus.

Build and operate NVIDIA Clouds Unified Access Management Platform (UAM). Design, code, and deploy scalable identity and access management features, integrate UAM across cloud services, collaborate with service teams, mentor peers, and participate in on-call support to ensure low-latency, high-availability authorization at scale.

Lead development of evaluation methodologies and KPIs for Waymo's behavior evaluation portfolio. Conduct deep-dive analyses, improve metrics, sampling and inference, and partner with product and engineering to build scalable monitoring and evaluation solutions that inform launch decisions and drive continuous improvement.

As a Senior Software Engineer in Robotics, you will transition Perception tasks to machine learning, focusing on system integration and optimization.

You will lead and manage a team of engineers developing machine learning solutions for perception product challenges in autonomous driving.