Machine Learning Jobs in San Jose, CA

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Develop efficient ML solutions for perception including sensor fusion, obstacle detection, scene understanding, and robust onboard systems.

Develop and refine robot behaviors and demonstration software for external events. Write behavior sequencing and control logic, test and debug on hardware, iterate quickly, and support event setup, rehearsals, and live troubleshooting. Collaborate with engineering and operations to deliver polished, reliable demonstrations.

Lead the product vision for innovative AI technologies, translating research into marketable products, and collaborating with cross-functional teams.

Seeking a Research Scientist to develop AI models for enhancing 3D reconstruction processes, requiring strong skills in computer vision and neural reconstructions.

Lead the unified Search and Voice product organization to define multi-year strategy and roadmap for the Roku Entertainment Assistant. Drive prioritization, cross-functional alignment, monetization, AI/ML innovation (LLMs, RAG, conversational AI), experimentation, quality, compliance, and measurable engagement and revenue outcomes while growing and mentoring a high-performing PM team.

The Applied Scientist will transform raw data into actionable insights by developing machine learning models for personalization, ranking, and optimization to enhance conversion and revenue. This role emphasizes building production systems, frameworks for experimentation, and communication with team members regarding technical challenges.

Lead technical engagement with MedTech customers to design and deploy AI and accelerated computing solutions. Develop proofs-of-concept, diagnose AI stack performance issues, collaborate with cross-functional teams, document and teach best practices, and build strategic partnerships to drive AI adoption in healthcare.

Develop and validate advanced driver assistance and autonomous driving systems, ensuring compliance with regulatory requirements while mentoring junior engineers.

The Demand Planner is responsible for improving forecast accuracy, analyzing market trends, and collaborating across functions to support supply chain effectiveness and strategic decision-making.

Seeking a Distinguished Technologist to lead AI architecture and design for HPE's Private Cloud AI offerings, focusing on applied AI, agentic AI, and generative AI. Responsibilities include technical leadership, implementation, and mentoring engineers while optimizing AI systems for cloud-based solutions.

Lead design and implementation of cloud-native, microservices backend systems that power AI-enabled features (LLM assistants, RAG pipelines, recommendations). Build scalable REST and event-driven APIs, establish MLOps practices, ensure reliability/security/observability, mentor engineers, and collaborate with product, scientific, and infrastructure teams to productionize AI services in regulated scientific environments.

The role involves applying machine learning techniques to circuit design, analyzing datasets, building and optimizing models, and collaborating with teams on various projects in VLSI and circuit design.

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.

Develop and deploy lightweight machine learning models for edge AI, working on hardware integration and optimization techniques while mentoring junior engineers.

Seeking a Machine Learning Engineer to build models from idea to delivery, maintain production models, and develop system architecture while collaborating on data-driven strategies.

Design, develop, and productionize scalable AI systems for autonomous threat detection and response. Build LLM-based reasoning engines, graph-based analytics, streaming feature pipelines, orchestration frameworks, and evaluation/reliability tooling. Collaborate with product, detection engineering, and customer teams to translate attacker behavior into robust ML and rule-based detections.

Lead the design, development, and implementation of advanced machine learning models, collaborating with teams to enhance services through AI/ML solutions.

Fine-tune and optimize LLMs (including multi-modal) for humanoid robots and autonomous vehicles; build scalable training and deployment pipelines; work on model efficiency (quantization, weight sharing), synthetic data generation, and cross-team product alignment.

The Technical Program Manager will lead software programs, collaborate with multiple teams, manage dependencies, and drive product delivery to improve engineering velocity and transparency.

Build and scale the infrastructure layer for visual intelligence: optimize VLM inference and GPU serving, design multimodal APIs and structured outputs, ensure scalable backend systems, reliability, observability, CI/CD, and developer experience. Drive 0→1 product work with strong testing discipline, schema validation, and performance optimization.

As a Senior Machine Learning Scientist, you will develop methodologies for advertising-related challenges, utilizing deep learning and statistical techniques for optimization and experimentation.

Architect and lead the development of a state-of-the-art inference platform focusing on low latency and scalability for advertising. Oversee performance optimization and collaborate with cross-functional teams.

Develop algorithms for RTL synthesis and digital logic optimization, utilizing machine learning and EDA techniques. Collaborate with design teams to implement solutions.

Design, build, and optimize scalable deep learning, reinforcement learning, and Bayesian models. Support research projects, develop end-to-end ML systems and software libraries, exploit parallel architectures, and collaborate with product and engineering to influence roadmap and present results.