Machine Learning Jobs in San Jose, CA

As an ML Engineer specializing in inference, you will optimize models for production, implement neural network techniques, and collaborate with researchers.

As an ML Engineer at Luma AI, you will design high-performance model serving pipelines, manage GPU resources, and optimize CI/CD for large-scale AI systems.

Work on vision-language model development and infrastructure: improve VLM understanding, OCR and function-calling workflows, fine-tuning recipes, evaluation and robustness, and optimize training/serving pipelines. Own end-to-end projects from experiments to production-ready code, collaborating on scale, cost, and reliability.

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.

Lead RTL development and microarchitecture for an AI-enabled RISC-V CPU core, including vector and matrix engines. Define features, optimize performance/power/area, support verification and simulation, collaborate with CPU modeling and physical design teams, and ensure design meets timing, reliability, testability, and power goals.

The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.

The role involves collaborating with customers to solve engineering challenges using machine learning, develop solutions, train teams, and improve products based on feedback.

Lead development of large-scale ranking, personalization, and recommendation systems for Roku's entertainment assistant. Design and deploy ML models (LLMs, transformers, RL, bandits), build feature platforms and data pipelines, run online A/B experiments, and mentor engineers while balancing engagement, retention, relevance, and monetization objectives.

Research and build efficient ML systems for large-scale LLMs and agentic RL: design algorithms and system techniques, prototype in training/inference stacks, run large-scale experiments, and translate findings into production or publications.

Lead workload-driven AI/ML architecture across hardware and software: characterize workloads, perform system-level performance and memory analysis, define measurement and pre-silicon estimation methods, leverage MLIR/IREE toolchains, identify bottlenecks, recommend SoC/CPU/memory optimizations, and present findings to engineering leadership while mentoring junior engineers.

Develop and deploy end-to-end AI/ML solutions focused on GenAI and LLMs. Build and fine-tune models, create embedding search and agentic systems, perform forecasting and data mining, collaborate with stakeholders, and drive improvements in supply chain and material demand forecasting.

The Director of AI/ML will lead software engineering teams in developing scalable AI-driven cybersecurity solutions, setting the technical vision and managing project execution while fostering innovation and collaboration.

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.

The role involves developing ML-based planning algorithms for autonomous trucks, ensuring safety checks, real-time operation, and keeping up with research. Requires strong communication and collaboration.

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.

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, 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.

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.

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.

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.

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

Drive the vision and development of machine learning models and algorithms, collaborating with teams and deploying solutions for business insights.

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