# Staff/Sr. Staff Machine Learning Engineer - World Modeling, Simulation at Waymo

Waymo is hiring a Staff/Sr. Staff Machine Learning Engineer - World Modeling, Simulation in Mountain View, United States. It pays $251k-$310k a year and Level rates it Builds AI ●●●●; you can [apply on Waymo](https://jobsbylevel.com/go/16dc9a8d-4423-4f04-9eab-279dedb79d3c?utm_medium=ai-agent).

AI Level 4, AI centrality 100 out of 100. Mountain View, CA, USA.

## Details

- Company: [Waymo](https://jobsbylevel.com/companies/waymo)
- AI level: AI Level 4 (score 100 out of 100)
- Location: Mountain View, CA, USA
- Salary: $251k-$310k
- Posted: March 6, 2025
- Apply: https://jobsbylevel.com/go/16dc9a8d-4423-4f04-9eab-279dedb79d3c?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=staff-sr-staff-machine-learning-engineer-world-modeling-simulation-at-waymo-d2160e

## Prepare for this job

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## Description

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 Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Simulator Team at Waymo builds state-of-the-art simulations of realistic environments for testing, training, and validation of the Waymo Driver. Our team is a multidisciplinary group of machine learning (ML) engineers, software engineers, and research scientists pushing the boundaries of autonomous systems. We develop industry-leading simulation platforms using large-scale generative and physical world modeling algorithms to model the physical world—encompassing dynamic agents, complex road topologies, multi-modal traffic systems, weather phenomena, and the vehicle’s full sensor suite (Camera, Lidar, Radar). To pioneer the next frontier of fidelity, scalability, and physical controllability, we are expanding our focus on foundation world models. In this role, you will define and drive the technical roadmap for large-scale generative architectures, bridging cutting-edge multimodal pre-training (across 4D video, LiDAR, and Radar), spatiotemporal tokenization, and causal post-training/distillation to generate interactive, multi-platform, safety-critical driving worlds for closed-loop evaluation and onboard model training. In this role, you will report to a Senior Staff Engineering Manager You Will: Own the Architectural Roadmap: Drive the technical vision and long-term roadmap for foundational 4D world modeling, unified spatiotemporal tokenizers (3D/4D VAEs), video generation architectures, and generative sensor simulation across Waymo -- enabling zero-shot generalization across next-generation vehicle platforms, sensor rigs, and international geographies. Lead Large-Scale Pre-Training: Architect, train, and scale foundation video and physical world models (including Sparse MoE and long-context block-sparse attention architectures) across distributed TPU/GPU clusters, establishing efficient training recipes, multimodal data curation strategies, and scaling laws. Drive Advanced Post-Training & Alignment: Design and implement robust post-training paradigms, including Supervised Fine-Tuning (SFT), Reinforcement Learning with AI/Critic Feedback (RLAIF), model merging/souping, and 3D/semantic conditioning to guarantee strict physical realism, counterfactual pose-divergence stability, and fine-grained control over safety-critical scene elements (signage, traffic signals, VRU gestures, and adverse weather). Integrate Multimodal & Physical Signals: Develop action-conditioned, 3D-grounded physical world models and multi-view generative pipelines that couple explicit geometric and physical priors (4D scene graphs, occupancy, vehicle kinematics, and interactive agent dynamics) with neural synthesis across Camera, LiDAR, and Radar—faithfully modeling physical sensor transport, occlusions, and causal state transitions. Optimize for Production & Serving: Scale inference and evaluation frameworks into production simulation infrastructure, applying few-step and causal autoregressive distillation (e.g., Distribution Matching Distillation [DMD], Self-Forcing, Consistency/Flow distillation), quantization, and KV-cached streaming inference for 10 Hz high-throughput closed-loop validation and RL gyms. Foster Research-to-Production Impact: Partner closely with Onboard Perception, Planner, and AI Foundation labs across Waymo and Alphabet (e.g., Google

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