Machine Learning Jobs in San Jose, CA

Engineer will leverage AI for pre-silicon verification, collaborate with teams, develop AI solutions, and engage customers regarding verification strategies.

As a Senior ML Infrastructure Engineer, you will design scalable architectures for ML, build robust data pipelines, manage GPU clusters, and drive improvements in CI/CD workflows while collaborating with cross-functional teams.

Build and operate production Kubernetes-based compute platform for large-scale data and ML workloads. Improve cluster reliability, GitOps delivery, multi-tenant scheduling, resource isolation, GPU workload orchestration, and reusable batch/workflow systems for petabyte-scale processing while contributing to QMS and continuous improvement.

Build core, shared ML capabilities for a production platform: foundation models for documents, extraction agents, model routing, unified evaluation, and continuous improvement pipelines. Own end-to-end capability development, production deployment, and cross-team support to improve accuracy, latency, cost, and reliability across institutional workflows.

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

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.

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.

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

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

As a Sales Executive, you will hunt for new clients, manage enterprise accounts, and create revenue streams while collaborating with internal teams.