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Senior ML Engineer builds and optimizes high-performance inference engines for autonomous vehicle ML models, balancing real-time onboard constraints with offboard data center demands using frameworks like JAX, XLA, and CUDA.
Develops and refines algorithms for autonomous vehicle motion planning, ensuring safe and predictable driving behavior in complex real-world scenarios using C++ and data pipelines.
Senior engineer building AI/ML infrastructure for autonomous-driving simulations, scaling multi-billion-parameter foundation models on large distributed systems.
Senior Software Engineer at Waymo optimizing distributed ML training pipelines for autonomous driving models using TensorFlow, JAX, and Grain.
Senior/Staff ML Engineer at Waymo building ultra-realistic 3D/4D world models and generative systems for autonomous vehicle simulation using advanced ML techniques like diffusion models and VLMs.
Lead the design and development of machine learning systems to evaluate and improve Waymo's autonomous driving technology, focusing on reinforcement learning, generative models, and large-scale simulation workflows.
Leads the design and implementation of evaluation systems for large vision and language models used in autonomous driving, building benchmarks to assess model quality, safety, and realism.
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo…
Designs and optimizes large vision-language and language models for autonomous driving, focusing on hardware-aligned architectures and on-device performance in a safety-critical environment.
Senior ML Engineer builds and deploys computer vision and vision-language models to generate high-fidelity labels for autonomous driving, using deep learning, generative AI, and reinforcement learning at scale.
Designs and builds reliability tools and architectures to prevent, detect, and debug software and hardware issues in autonomous vehicle compute platforms, using C++, CUDA/GPU/TPU, and LLVM/XLA.
Leads cross-functional programs to integrate simulation into machine learning workflows for autonomous driving, coordinating releases and scaling ML-based evaluation tools.
A senior machine learning engineer at Waymo optimizes generative models and simulations for autonomous driving, focusing on performance, efficiency, and scalability using advanced ML techniques and hardware accelerators.
Build and scale large vision-language foundation models for autonomous driving, using multimodal pre-training and reinforcement learning to improve scene understanding and autolabeling.
Build and scale ML systems that continuously improve autonomous vehicle perception models using active learning, data curation, and large-scale pipelines.
Staff Tech Lead ML Engineer on Waymo's Perception team, designing multi-sensor model architectures for autonomous vehicle scene understanding and optimizing models for onboard compute using Python, C++, and frameworks like PyTorch/JAX/TensorFlow.
Senior ML engineer on Waymo's Special Vehicle Compliance team building multimodal perception for autonomous vehicles: training multi-task deep learning models (PyTorch/JAX) that fuse camera, LiDAR, and audio, developing high-resolution vision transformer backbones, and running large-scale data mining/auto-labeling pipelines, with model optimization for onboard accelerator inference.
Senior ML engineer on Waymo's Special Vehicle Compliance team, building learned driving policies that bridge perception and planning for autonomous vehicles. Day-to-day involves training, evaluating, and deploying decision-making models into production using PyTorch/JAX, RL/imitation learning, and closed-loop simulation.
Waymo is hiring a Sr Staff Tech Lead / ML Engineer for its Perception team to set the technical roadmap for next-generation multi-modal perception of the Waymo Driver - architecting scalable sensor-fusion models, optimizing them for onboard hardware, leading cross-functional initiatives, and mentoring engineers. Core stack: Python, C++, and modern ML frameworks like PyTorch, JAX, and TensorFlow.
