Machine Learning Jobs in Sunnyvale, CA

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.

Build and maintain MLOps infrastructure: CI/CD pipelines, containerize and deploy ML models, manage training and batch prediction workflows, and ensure scalable, highly available production ML systems while collaborating with researchers and data scientists.

Develop and deploy perception ML models for autonomous vehicles (detection, sensor fusion, tracking, classification, intent). Own end-to-end lifecycle: POC, model iteration, onboard deployment, monitoring, and optimization for latency and scale.

The Performance Engineer - Inference will optimize model inference speed and throughput, debug low-level kernel performance, and develop tools to visualize performance data.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly detection, visualization and analysis tools, and frameworks for failure classification and regression detection. Extend compilers, runtimes, and instrumentation for advanced profiling. Improve bring-up and low-level debug workflows, partner cross-functionally across hardware, firmware, compiler and runtime teams, lead high-impact initiatives, and support incident response and long-term corrective actions.

Optimize and deploy ML models to production embedded runtimes. Improve inference latency and efficiency across accelerators, prune/quantize models for memory‑constrained platforms, profile on target hardware, and collaborate with ML and software teams on architecture and integration.

Build and operate end-to-end ML infrastructure: distributed cloud GPU training, datasets, training frameworks, evaluation, deployment, and integrate pipelines into product workflows while collaborating with modeling teams.

Develop, prototype, evaluate, and deploy ML-first behavior prediction and planning models to forecast motion and interactions of road users. Build software and infrastructure for behavior-specific ML, collaborate with perception and planning teams, and productionize models for autonomy systems.

Integrate state-of-the-art machine learning (especially generative) into production-grade sensor simulation for LiDAR, Radar, and Camera. Collaborate with research, rendering, and physics teams to develop, evaluate, and deploy models and tooling that produce production value and customer impact.

Lead design, development, and deployment of deep learning models for pathology image analysis. Drive experiments, optimize models for production, support data and ML infrastructure, implement semi/self-supervised methods, collaborate with clinical and product teams, and ensure validated, scalable medical device software.

Optimize and benchmark deep learning training and inference stacks on HPC/GPU hardware; profile and triage system bottlenecks; build automation tools; develop kernels and systems for new models; lead design reviews, review code, document work, and represent the lab at conferences.

Design, extend, and maintain distributed training infrastructure: modify frameworks (DeepSpeed, FSDP, etc.), implement distributed optimizers, build multi-node launch/config systems, implement experiment tracking and monitoring, optimize reliability and performance, and write production-quality ML infra code in PyTorch or JAX.

Build and productionize distributed pre-training stacks (PyTorch/JAX) at multi-node GPU scale, implement new optimizers and high-performance CUDA/Triton kernels, optimize mixed-precision training and memory/communication, develop experiment infrastructure and monitoring, and mentor junior engineers to accelerate foundational model research.

Responsible for designing and developing scalable data pipelines, storage systems, and monitoring tools for ML data infrastructure, ensuring system integrity and performance evaluation.

Design and develop data pipelines for autonomous driving systems, create storage for data metrics, build dashboards, and maintain data integrity, focusing on ML components.

Apply deep learning to improve quality and accuracy of labeled multi-modal sensor data for simulation. Build models to identify and correct perception-data issues, analyze data quality and anomalies, collaborate with perception and simulation teams, and lead ML strategies for high-fidelity data generation and labeling pipelines.

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.