Machine Learning Jobs in Santa Clara, CA

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.

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.

Own, design, build, and operate core components of a high-throughput, low-latency real-time ad decisioning system. Take end-to-end responsibility for subsystems (architecture, implementation, testing, deployment, on-call), diagnose concurrency/timing issues, collaborate with applied scientists on ML-driven decisioning, mentor engineers, and drive system evolution and operational excellence.

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.

As a Sr. Staff Machine Learning Engineer, you will drive the development of cloud security features, leveraging AI/ML to solve real-world challenges and optimize production systems.

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.

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.

Architect role to design full-stack, large-scale solutions informed by leading tech companies. Requires deep ML/AI and large-data analysis knowledge, proficiency across languages/frameworks (Go, Scala, Python, R, MATLAB, Node.js, React), NoSQL expertise, and familiarity with mobile testing, deployment pipelines, and SRE practices. US work authorization required.

The Director of AI will lead AI and data engineering teams, define and execute AI strategy, and ensure effective partnership across functions, focusing on scalable AI solutions and responsible AI practices.

The Senior ML Infrastructure Engineer will design, implement, and optimize computing platforms for simulation workflows, ensuring high reliability and performance. Responsibilities include leading technical decisions, collaborating with ML engineers, and enhancing system efficiency.

Lead development of large-scale reinforcement learning frameworks and simulation pipelines for autonomous vehicle foundation models. Design closed-loop simulation, optimize reward functions and simulation quality, build scalable data-processing workflows, and improve training reliability on GPU clusters. Develop monitoring and debugging tools while collaborating with platform, product, and research teams to integrate advanced model architectures and productize machine learning solutions.

Lead product strategy and execution for AI-native, self-driving enterprise wireless networks. Engage customers, define roadmaps, validate concepts, and partner with engineering, AI/ML, and cloud teams to deliver automation, observability, predictive insights, and operational troubleshooting capabilities that simplify WLAN management and improve network resiliency.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.

Assist in developing software for autonomous driving vehicles, participating in project work involving machine learning, coding, and testing.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

The intern will design and implement AI-driven automation workflows to enhance the silicon testing process, collaborate with engineers, and document automation recipes.

The engineer will leverage AI to enhance pre-silicon verification processes, collaborate with teams, and improve methodologies using advanced tools.