Machine Learning Jobs in Sunnyvale, CA

You will lead and manage a team of engineers developing machine learning solutions for perception product challenges in autonomous driving.

Entry-level engineer working at the software/hardware interface: kernel development, ASIC performance modeling, system bringup, performance profiling and debugging. Learn Cerebras Wafer Scale Engine end-to-end, model and tune performance, and validate designs on hardware and simulators using C++ and Python.

The Senior ML Infrastructure Engineer will design, implement, and optimize computing platforms for simulation workflows, ensuring high reliability and performance. Responsibilities include leading technical decisions, collaborating with ML engineers, and enhancing system efficiency.

Lead development of large-scale reinforcement learning frameworks and simulation pipelines for autonomous vehicle foundation models. Design closed-loop simulation, optimize reward functions and simulation quality, build scalable data-processing workflows, and improve training reliability on GPU clusters. Develop monitoring and debugging tools while collaborating with platform, product, and research teams to integrate advanced model architectures and productize machine learning solutions.

The Principal Machine Learning Infrastructure Researcher will design and model performance for ML systems using Lightmatter technology while leading a small team and publishing research findings.

Lead product strategy and execution for AI-native, self-driving enterprise wireless networks. Engage customers, define roadmaps, validate concepts, and partner with engineering, AI/ML, and cloud teams to deliver automation, observability, predictive insights, and operational troubleshooting capabilities that simplify WLAN management and improve network resiliency.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.

Lead the design and implementation of advanced ML models for autonomous driving, collaborating with teams, mentoring engineers, and driving technical initiatives.

Lead development of multi-sensor, multi-task machine learning models for autonomous driving perception, including object detection, occupancy prediction, road understanding, and trajectory planning. Architect foundational modeling libraries, sensor fusion components, temporal aggregation systems, and scalable experimentation pipelines. Optimize models for latency, throughput, memory, and onboard hardware constraints while enabling rapid prototyping and analysis of model training and learned representations.

Develop and optimize deep-learning and computer-vision models for autonomous vehicles in construction and agriculture. Build multimodal models using vision, radar, and thermal data; research methods to improve accuracy and speed; transition algorithms to real-time robotic platforms; and maintain data-processing, annotation, training, evaluation, and deployment pipelines.

Assist in developing software for autonomous driving vehicles, participating in project work involving machine learning, coding, and testing.

This role involves driving AI strategy and infrastructure investments at Google DeepMind, overseeing high-stakes decisions that shape Google's AI product portfolio and future.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

Lead technical vision and strategy for next-generation graphics developer tools. Design and prototype tooling, drive cross-team initiatives, mentor engineers, engage external developers and partners, and deliver production-ready solutions that improve graphics development, debugging, profiling, and optimization workflows.

Lead end-to-end supply planning strategy and governance for networking products (optics, switches, adapters). Build scalable planning frameworks across chips, memory, capacity, and manufacturing; ensure data integrity (BOMs, yields, cycle times); accelerate time-to-market via complexity reduction and AI-enabled planning; and manage and develop global supply planning teams.

The Curriculum Product Manager will collaborate with AI experts to create courses, ensure content quality, and enhance learning experiences in AI and machine learning.

As a Senior Technical Recruiter, you will design and execute hiring strategies to attract software engineering talent, manage multiple searches, and mentor other recruiters.

Lead a team of engineers in developing offline perception algorithms and data mining tools to produce high-quality data for autonomous driving.

Develop and productionize ML models for assessing autonomous vehicle behavior, build ML infrastructure, and collaborate across teams for deployment.

As a Sr. Software Engineer, you'll enhance Databricks' customer support with AI-driven systems, ensuring improved automation and user experience across support engineering operations.

Lead strategy, roadmap, and delivery of AI-driven networking products using ML, LLMs, generative and agentic AI. Translate customer needs into product requirements, define KPIs, align cross-functional teams, drive go-to-market and thought leadership, and mentor product managers.

Design and validate state-of-the-art classification algorithms for EEG and neurological indications using signal processing, time-series analysis and machine learning. Analyze large biomedical datasets, select evaluation metrics, document models for regulatory compliance, collaborate with clinical stakeholders, mentor junior staff, and manage multiple projects from concept to deployment.

Design, implement, and verify real-time embedded Linux software for high-definition stereo surgical video and intra-body illumination. Integrate and evaluate cameras, image sensors, and video-processing pipelines; extend frameworks to new hardware; build diagnostic and simulation tools; participate in risk analysis, system integration, and product testing for mission-critical medical devices.

Develop and validate compact transistor models (FinFET, GAA) and simulation methodologies for PDKs. Collaborate cross-functionally to ensure modeling readiness, perform statistical, RF and EM simulations, automate analysis with Python, and integrate innovative techniques like OMI and ML to meet delivery milestones.

The VP of Product will lead CoreWeave's Research Training Stack, focusing on enhancing AI infrastructure, ensuring quality for pre-trained models, and connecting researchers with advanced cloud capabilities.