Machine Learning Jobs in Sunnyvale, CA

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

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.

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.

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

Lead design, build, deploy, and maintain production-grade AI/ML systems (LLMs, agentic architectures, classical ML). Provide hands-on technical leadership across architecture, model selection, implementation, testing, deployment, observability, and operational support while collaborating with data scientists, platform and security teams and mentoring engineers.

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.

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.

Design and implement AI-driven vehicle intelligence features using telemetry, user behavior, navigation, and fleet data. Apply reinforcement learning, forecasting, planning, personalization, and simulation to optimize energy, comfort, charging, and routing. Validate in simulation and integrate models into vehicle and cloud platforms focusing on latency, efficiency, safety, explainability, and OTA readiness while collaborating with autonomous driving, product, UX, and systems teams.

Design and develop ML/AI vehicle intelligence features using telemetry, navigation, user behavior, and fleet data. Build predictive, optimization, personalization, and RL-based models; validate via simulation and digital twins; integrate models into vehicle and cloud platforms with attention to latency, compute efficiency, safety, and OTA readiness. Collaborate cross-functionally with product, UX, systems, controls, and autonomous driving teams.

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.

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 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 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.

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.

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.

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.