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# Senior Machine Learning Engineer, Scenes, Traffic Controls

- **Company:** Waymo LLC
- **Location:** Mountain View, CA, United States
- **Experience:** Expert
- **Salary:** $213,000.0 - $263,000.0
- **Contract:** Permanent contract
- **Skills:** Computer Vision, C++ (Programming Language), Information Engineering, Data Mining, Device Drivers, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, SQL Databases, Pytorch, Apache Spark, Apache Flume, Machine Learning Operations, Multiaccess Edge Computing
- **Published:** November 2, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-machine-learning-engineer-scenes-traffic-controls-mountain-view-ca--278e1908-7dd3-4f92-84b0-43c976c78749

## About the Role

Machine Learning, Software Engineering, Autonomous Driving Systems, Use Cases, Process Improvement, Bridge Building, Device Drivers, Automotive Automation, Machine Learning, Performance Management, * BS/MS/PhD in CS, EE, Robotics, ML, or a related field, or equivalent practical experience.

 * Hands-on experience shipping ML models to production, including training, offline evaluation, and deployment (L4: 2+ yrs; L5: 4+ yrs, or a PhD plus 2+ yrs)
 * Strong data mining / data engineering: building large-scale batch pipelines (Beam/Flume/Spark or similar) and analytical SQL to find, slice, and curate data
 * Proficiency in Python with a modern DL framework (JAX, TensorFlow, or PyTorch) and production C++
 * Demonstrated cross-stack generalist work: has owned problems that crossed data, model, and systems/serving boundaries
 * Rigorous evaluation instincts: metric design, error analysis and slicing, and A/B or staged rollout

We prefer:

 * AV/Robotics experience, especially perception (detection, classification, tracking)
 * Experience with transformer or temporal models (GRU/RNN), multi-task learning, and model calibration/uncertainty
 * Labeling-pipeline and supervision design; autolabeling or synthetic data
 * TPU/accelerator performance work; exporting models for onboard/edge inference
 * Builds internal tools (visualization, triage, experiment tracking) that accelerate the team

## 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.

Driver Refinement and Validation's (DRV) mission is to build upon the high-quality ML Driver developed by Driver Intelligence, refining its performance to improve autonomous driving in specific, complex real-world contexts and problem areas (e.g., Ride-Hailing contexts). It aims to ensure that the Waymo Driver always computes safe, compliant, comfortable, and useful plans. As a Machine Learning Engineer in DRV, you will bridge the gap between cutting-edge machine learning and real-world vehicle performance, engineering the validation and onboard systems that ensure the autonomous vehicle reliably executes intelligent and predictable maneuvers.

You will:

 * Own ML features for Scene Understanding (traffic controls, signs, special regions such as RRX and flood zones, lane states) end to end: data model eval onboard launch
 * Mine and curate large-scale driving data (real and simulated) to find long-tail failures, build training and eval sets, and design labeling and supervision
 * Design, train, and iterate on model tasks and heads in large multi-task models. Improve calibration, robustness, and generalization to new geographies
 * Build rigorous evaluation: PRT metrics, slicing taxonomies, side-by-sides, and staging launches, and turn the results into launch decisions
 * Integrate models into the onboard stack (C++), combining learned outputs with safe heuristic fallbacks, Mendel-guarded rollouts, and map-contradiction handling
 * Triage real driving events, find the root cause anywhere in the stack, and propose surgical or systemic fixes. Work closely with Perception, Reasoning, Mapping, and ML Infrastructure teams