Machine Learning Jobs in San Jose, CA

As a Senior Staff Software Engineer, you will develop and maintain AI deployment software, optimizing hardware-software co-design, and work closely with various experts to enhance infrastructure.

Lead research, design, and implementation of classical and ML/DL computer vision algorithms for defect detection, classification, metrology, and image analysis on semiconductor inspection tools. Prototype and evaluate approaches, run feasibility studies and benchmarking on large datasets, optimize performance for available compute (GPU), write production-quality code, and collaborate with software, systems, and compute infrastructure teams to productize solutions.

Define and execute the scientific roadmap for search and recommendation: architect large-scale ranking and retrieval systems, develop and distill Ranking Foundation Models, integrate vector/RAG/hybrid retrieval into production, partner with product and engineering, and lead and scale multidisciplinary ML teams to optimize marketplace health.

Design, train, and optimize ML models for LiDAR perception (detection, segmentation, tracking). Build end-to-end ML workflows (data pipelines, training, metrics, reporting). Productize models into NVIDIA DRIVE AV with efficient C++ production code. Collaborate with sensor teams and engineers, adopt new ML techniques, and optimize models for real-time embedded inference.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

Design and build machine-learning-driven robotic systems that operate in the real world, support portfolio company growth, influence product and technical direction, and advance company and career outcomes.

Senior Machine Learning Engineers will develop ML products and lead projects end-to-end—from initial concept through final launch—across S32 portfolio companies. Candidates should drive product direction, have strong ML engineering and productization experience, and make significant business impact.

Lead RTL development and microarchitecture of an AI-enabled RISC-V CPU core, including vector and matrix engines. Drive design for performance, power, area, verification support, and SoC integration while collaborating with cross-functional teams to meet timing, reliability, testability, and power goals.

Design and deploy machine learning systems for autonomous vehicles, collaborating with teams to improve model performance and safety using real-world data.

Develop and deploy ML-based perception algorithms for robotics, optimize pipelines, and collaborate across disciplines for integration.

As a Senior Machine Learning Engineer, you'll design and implement large-scale E2E driving models, enhance data workflows, and collaborate with cross-functional teams to advance autonomous driving technologies.

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

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.

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.

The role involves developing machine learning algorithms to enhance customer experience. Responsibilities include end-to-end ML lifecycle management, validating models, and collaborating with teams to address complex problems.

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.

The Machine Learning Engineer will research and develop AI-driven solutions to analyze customer data, optimize ML models, and collaborate with cross-functional teams.

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.

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.

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.

Develop and maintain machine learning models for robotics applications, collaborate across teams, and contribute to innovative solutions in AI and mobility.

Develop, train, and optimize deep learning models for autonomous trucks (detection, tracking, mapping, end-to-end planning). Execute full ML lifecycle from data curation to deployment, collaborate with simulation/infrastructure/planning teams, and evaluate SOTA computer vision and generative AI methods to address real-world corner cases.

Lead institutional research, reporting, and business intelligence to support planning, accreditation, enrollment, and compliance. Supervise IR/analytics staff, develop dashboards and predictive analyses, manage state/federal reporting, and advise leadership using data-driven insights.