Machine Learning Jobs in Sunnyvale, CA

Lead design and deployment of scalable auto-labeling pipelines and automated graders. Train, optimize, and productionize state-of-the-art 2D/3D computer vision models and VLMs. Set ML strategy, mentor engineers, and collaborate with Perception, Planner, and Simulation teams to meet labeling needs.

As a Machine Learning Research Scientist, you will develop advanced algorithms for behavior planning and prediction for autonomous vehicles, focusing on machine learning techniques to solve real-world challenges in trajectory generation and motion planning.

Lead development and productionization of LLM quantization and deployment pipelines (PTQ, QAT, mixed-precision). Implement robust Python production code, build export/calibration/benchmarking workflows, collaborate across research, field-testing, vehicle software, and training infrastructure teams, and curate evaluation datasets and metrics to validate model accuracy, latency, and numerical consistency for on-vehicle inference.

Lead development and productionization of LLM quantization and deployment pipelines (PTQ, QAT, mixed-precision, INT8/FP4), build robust export/calibration/benchmarking/validation tooling, collaborate with research, field-testing, simulation and vehicle software teams, curate evaluation datasets and metrics, analyze numerical errors and performance trade-offs, and develop orchestration workflows for on-vehicle inference and model sign-off.

Design and scale data pipelines and automated closed-loop systems for continuous improvement of perception models. Deploy active learning, curate and prune large datasets, develop evaluation metrics and ground-truth‑free performance measures, and build robust mining, training, and evaluation pipelines for rare, safety-critical scenarios while collaborating with research and operations teams.

The role involves designing de novo frameworks for molecular engineering, collaborating across teams to optimize data-driven biological experiments and integrating ML models with experimental systems.

The AI Research Scientist will develop and evaluate generative models for scientific data, collaborate with teams to apply AI in materials science, and mentor junior members while staying current in the field.

This role involves optimizing inference performance, managing high-performance systems, scaling models, and full-cycle ownership from research to production in a fast-paced environment.

Design and implement efficient algorithms to optimize LLM inference on DNN accelerators. Collaborate with mathematicians, ML researchers, and engineers to apply advanced algorithmic and numerical techniques for high-impact generative AI performance improvements.

Develop and train computer vision and multimodal models, design scalable AI frameworks, and improve systems through fine-tuning and reinforcement learning techniques while collaborating with multiple teams.

Lead the development and deployment of large-scale machine learning models, optimize model performance, and enhance collaboration across teams while mentoring junior engineers.

Lead development of ML components for medical image reconstruction: define training/evaluation pipelines and metrics, productionize models (inference performance, monitoring, safe fallbacks), collaborate on hybrid physics-ML algorithms, and build tooling for experimentation and rigorous verification.

Design, build, and operate large-scale offline data infrastructure and pipelines for ML training and experimentation. Improve reproducibility, observability, performance, and cost-efficiency across batch and streaming workloads while integrating orchestration systems and partnering with ML engineers.

Lead and build a research-focused ML team to develop post-training/RL, dataset curation, LLM pretraining, and sparsity techniques; adapt algorithms to Cerebras hardware, run systematic model evaluation, and collaborate with internal teams and external partners to drive production deployments and hardware/software co-design.

Join the SOTA Training Platform team to bring up ML models on Cerebras systems, enhancing performance across the software stack and debugging issues.

The role involves designing AI models and training methods leveraging wafer-scale hardware and collaborating with various teams and partners in AI and computational science.

The R&D Engineer will design and implement performance workloads on Cerebras' wafer-scale hardware, collaborating across teams to advance AI and scientific computing. Responsibilities include optimizing performance models, contributing to the technology roadmap, and publishing research findings.

Design and optimize pre-training pipelines for VLM models, conduct research on pre-training techniques, and evaluate performance for autonomous driving.

Design and implement compiler optimizations and new compiler features for a high-performance neural network inference platform. Analyze generated code performance, collaborate with hardware architects for hardware/software codesign, and work with model developers to tune networks for inference efficiency and accuracy. Leverage agentic AI to accelerate analysis, planning, and production software improvements.

Build and operate large-scale ML training infrastructure: distributed GPU training, multi-cluster scheduling, data pipelines (batch and streaming), ML workflows, observability, reliability, and on-call incident response to keep autonomy model training running efficiently.

Build and operate large-scale ML training infrastructure: distributed GPU training, multi-cluster scheduling, data pipelines (batch & streaming), ML workflows, observability, alerting, and on-call/incident response to ensure reliable, cost-effective model training and releases.

Design, build, and maintain ML infrastructure for model lifecycle: training, optimization, validation, deployment, inference, and observability. Own ML inference and compiler platforms, develop scalable ML pipelines, continuous testing/monitoring, and support deployment to diverse hardware for autonomous vehicles.

Build and maintain core ML infrastructure: design and implement ML training, optimization, validation, and deployment pipelines; operate an in-house ML inference and compiler platform; implement testing, monitoring, and observability for model lifecycles; collaborate with cross-functional teams to deploy optimized models to vehicle hardware.

Lead design and implementation of agentic AI systems in the Unity engine, developing scalable RL and planning frameworks, integrating ML models under real-time constraints, establishing evaluation and safety practices, and mentoring engineers to deliver reusable, performant agent behavior for games and simulations.

Lead technical direction and implementation of computer vision and multi-modal ML models (transformers, diffusion, VLMs, JEPA) from research to production. Define model architecture, training, evaluation, and deployment across cloud and on-device targets. Mentor engineers, set engineering standards, partner with research and platform teams, and optimize for latency, memory, and cost to deliver scalable AI-driven game experiences.