Senior Software Engineer, ML Evaluation (Infrastructure)

Waymo•San Francisco, CA

35d•Hybrid

About The Position

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 Predictive Planning team (PrePlan) develops and deploys state-of-the-art machine learning solutions that predict the future state of the world and plan the Waymo Driver's behavior. Our mission is to transform Waymo's unprecedented scale of driving data into robust, generalizable, and performant deep neural networks. These models enable the autonomous vehicle to navigate complex environments safely and efficiently. The team's work touches on all of the most exciting aspects of machine learning, including but not limited to Large Language Models (LLMs), Reinforcement Learnings (RL), and Vision understanding. In this role, you will ensure that our systems remain reliable in the face of this rapid, experiment-driven development. In this hybrid role, you will report to a Technical Lead Manager.

Requirements

B.S. or higher in Computer Science, Math, or equivalent real-world experience
4+ years building and maintaining high-scale distributed or ML inference systems
Coding and testing skills, specifically Python/C++
Familiarity with large-scale fleet management, testing, and deployment, e.g.: GCP/AWS/Kubernetes, Jenkins, GCS/S3, etc.
Familiarity with large-scale ML tools, e.g.: tf.serving/Torchserve, Kubeflow/Sagemaker Pipelines/Vertex AI Pipelines, etc.
Understanding of Machine Learning fundamentals and experience with popular ML frameworks such as JAX, PyTorch, or TensorFlow.

Nice To Haves

Experience developing and maintaining evaluation pipelines for ML models
Experience deploying and supporting machine learning models for computer vision, natural language processing, robotics/motion planning, or recommendation systems.
Experience supporting a large team of MLEs developing high-capacity, production-grade models and components
Strong understanding of metrics computation and regression detection at scale

Responsibilities

Collaborate with Machine Learning Engineers, Data Scientists, and ML Infrastructure Engineers to surface signals on model, component, and system performance
Develop, scale, and maintain CI/CD pipelines for automating metric computation, aggregation, and monitoring
Build data management tooling to improve reproducibility and scale analyses automatically as Waymo drives more miles
Collaborate with ML and product teams to define evaluation criteria and translate those into metrics and targets
Stay current with emerging technologies and trends in ML evaluation and metrics design.
Be aware of best practices used in the Alphabet stack of ML technologies (e.g. TF, JAX, Flax, Beam etc).

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