Machine Learning Jobs in Sunnyvale, CA

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.

Optimize inference performance of large multimodal foundation models across cloud and on-robot targets. Diagnose bottlenecks, apply quantization/pruning/distillation, tune kernels (CUDA/Triton), build benchmarking and regression detection, and translate research models into deployment-ready implementations.

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.

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.

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.

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.

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 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.

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

Design and scale data pipelines and ML infrastructure to train and deploy perception models on edge and cloud, integrate foundation models to automate labeling, run software- and hardware-in-the-loop testing, reduce annotation costs, accelerate model iteration, and support DoD field events.

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.

The role involves developing ML systems for enterprise workflows, focusing on user understanding, task prediction, and optimizing ML pipelines. Key responsibilities include designing scalable evaluation frameworks and collaborating with teams to deliver production-ready solutions.

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 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.

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.

As an ML Engineer - Robotics, you'll design, train, and deploy intelligent models for autonomous systems, focusing on perception, planning, and decision-making pipelines, while collaborating with robotics engineers to evaluate model performance.

As a Machine Learning Intern, you'll design, train, and evaluate ML models, work on data preprocessing, develop pipelines, and assist with model deployment while collaborating with experienced engineers.

The Founding ML Engineer will build and scale machine learning systems, focusing on ML pipelines, model implementation, and collaboration with teams to drive measurable impact.

The Founding ML Research Engineer will design and evaluate ML models, develop experimentation pipelines, and contribute to AI research while optimizing training processes.