Machine Learning Jobs in Sunnyvale, CA

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.

As a Staff ML Software Engineer, you will lead the development and deployment of machine learning applications, leveraging your expertise to drive software architecture decisions, maintain cloud infrastructure, and collaborate within a small, agile team focused on geospatial challenges related to climate change.

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

As a Data Science and Machine Learning Software Engineer at NetApp, you will develop algorithms for real-time personalized search and recommendation using IoT data, while working on cloud-based services and analytics.

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 the development of AI/ML deployment infrastructure, collaborate with engineers, develop pipelines, manage teams, and enhance data applications.

Develop AI/ML models for 3D simulation and geology prediction, refine data pipelines, and integrate deep learning tech with product backend.

As an Applied Scientist (ML), you will develop ML solutions, analyze existing systems, and create impactful ML prototypes and evaluation datasets.

You will create large datasets and training recipes, develop methods for data mining, create scalable infrastructure solutions, and collaborate with ML infrastructure teams.

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.

Responsible for software integration and quality for Cerebras AI platform, focusing on automation, debugging, and cross-team collaboration. Develops QA strategies and testing methodologies to ensure product quality.

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 maintain ML infrastructure for training, serving, and monitoring models for Ads and Recommendations. Improve training reliability and serving performance, own core ML infra components (pipelines, feature/embedding platform), address production issues, and collaborate with ML engineers to deliver robust model pipelines.

Conduct ML research and applied work on LLM trust and safety (prompt injection, alignment, privacy, interpretability). Design and run experiments, read/write research papers, fine-tune and evaluate deep models, and implement research code using PyTorch/HuggingFace tools. Collaborate with engineers to productionize research; related student research topics may be considered.

Build and deploy large-scale ML systems for search, ranking, and personalization. Process billions of events and petabytes of logs into consumable features, train transformer and embedding models, serve models in real time, track impact metrics, and collaborate cross-functionally while mentoring engineers.

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.

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.

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

Lead post-training for LLMs (CPT, SFT, RL) focusing on RL methods; design and curate training and reward data; run and debug distributed training on mid-to-large GPU clusters; build evaluation and verifier pipelines; partner with product and business teams to deliver targeted model improvements quickly.

Build and productionize large-scale data pipelines and platforms to generate ML and evaluation datasets from driving logs and simulation data. Design tools for data mining, exploration, and analysis, and collaborate with Data Science and Quantitative Analytics to deliver high-impact, production-ready infrastructure and dataset generation.

Collaborate to develop generative models for autonomous driving, focusing on plan generation and optimizing models through reinforcement learning.