Staff/Senior Staff Machine Learning Engineer (World Modeling, Simulation)

Join Waymo, a leader in autonomous driving technology. As a Staff/Senior Staff Machine Learning Engineer, you will drive the technical roadmap for large-scale generative architectures and lead the development of industry-leading simulation platforms. You will work closely with multidisciplinary teams and have a significant impact on the future of autonomous systems.

Define and drive the technical roadmap for large-scale generative architectures, bridging cutting-edge multimodal pre-training and causal post-training/distillation.

About Waymo

Framework Proficiency: Expert-level proficiency in Python and modern distributed ML frameworks (such as JAX/Flax, XLA/Pallas, PyTorch, Megatron-LM, Pathways, or DeepSpeed) for multi-node model, sequence, and data parallelism

Domain Pedigree: Prior experience from leading video generation labs, foundation model startups, advanced robotics institutes, or high-tier academic research groups focusing on physical AI, foundation video models, or world simulation

Responsibilities

• Architect, train, and scale foundation video and physical world models across distributed TPU/GPU clusters, establishing efficient training recipes and scaling laws.
• Design and implement robust post-training paradigms, including Supervised Fine-Tuning, Reinforcement Learning with AI/Critic Feedback, and model merging/souping.
• Post-Training & Alignment Expertise: Hands-on experience designing post-training and student-conversion pipelines for generative models—including few-step diffusion/flow distillation (e.g., DMD, MMD, Consistency Models, Rectified Flow), autoregressive exposure-bias mitigation (e.g., Self-Forcing), SFT, and Reinforcement Learning (RLAIF / reward-model alignment)—to control and stabilize long-horizon rollouts
• Deep 3D/4D Vision Understanding: Deep technical foundation in 3D computer vision and physical world modeling—including action-conditioned scene dynamics, explicit 3D physical/geometric priors (4D occupancy, dynamic meshes, rigid/articulated kinematics), spatiotemporal tokenization (3D/4D causal VAEs), continuous ray/camera geometry (e.g., Plücker rays, cross-rig calibration), and generative 4D world representations (coupling explicit physical priors with neural generation)
• Large-Scale Pre-Training Track Record: Demonstrated experience training large-scale generative and physical world models from scratch (e.g., action-conditioned 3D/4D world models, large video diffusion models, autoregressive spatiotemporal transformers, flow matching, or vision-language-action models) on high-performance compute clusters (multi-node GPU/TPU)
• Multi-Modal Sensor Simulation: Background in generative modeling and synthesis of non-RGB physical sensors—specifically 3D LiDAR point clouds/range images and 4D imaging radar—alongside multi-camera automotive rigs across diverse vehicle platforms, weather, and lighting conditions
• Embodied AI & World Dynamics: Hands-on experience in physical world modeling and embodied dynamics—including action-conditioned causal world models, differentiable or hybrid physics engines, rigid/articulated body kinematics, vehicle dynamics, interactive multi-agent traffic dynamics, or closed-loop robotics simulators

Requirements

• Education: PhD or MS in Computer Science, Machine Learning, Robotics, Computer Vision, or a related quantitative field (or equivalent practical research/industry tenure, including academic appointments looking to transition to industry)
• Senior Technical Leadership: 8+ years of industry or relevant research lab experience (or tenure-track/associate professorship equivalent) developing, deploying, and leading complex ML architectures
• High-Performance Infrastructure: Experience with low-level systems optimization, XLA/Pallas TPU kernels or custom CUDA/Triton kernels, block-sparse / Flash / Splash attention over 100k+ token sequences, and high-throughput KV-cached distributed serving
• Systems Engineering: Working proficiency in modern C++ for high-performance simulation integration

Pay

USD 251,000 - 310,000