Technical Lead Manager, Machine Learning Runtime & Serving

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.

Waymo is seeking a senior Technical Lead Manager (TLM) Machine Learning Engineer to guide the technical vision of our core ML infrastructure. In this role, you will actively grow and manage a high-performing team of 6 engineers to deliver Waymo’s next-generation ML ecosystem. This critical work encompasses both the in-vehicle inference engine and the cloud-based serving infrastructure for our foundational models. You will architect scalable, high-performance ML runtime systems that operate across two extreme domains: the highly constrained edge compute environment of autonomous vehicles and our large-scale, offboard data centers.

You will:

• Guide the technical vision of our core ML infrastructure while actively growing and managing a high-performing team of 6 engineers to deliver Waymo’s next-generation ML ecosystem, encompassing both the in-vehicle inference engine and the cloud-based serving infrastructure for our foundational models.
• Architect scalable, high-performance ML runtime systems that operate flawlessly across two extreme domains: the highly constrained edge compute environment of autonomous vehicles and our large-scale, offboard data centers.
• Navigate complex engineering trade-offs, driving feature development that seamlessly balances the strict, real-time latency and memory limits of onboard execution with the high-throughput, highly concurrent demands of fleet-scale cloud serving.
• Spearhead the strategic transition of core ML workloads to a JAX-native runtime architecture, which includes actively extending and modifying underlying ML compilers and runtimes (e.g., OpenXLA/PjRT, TensorRT).
• Partner across organizational boundaries with world-class ML researchers in Perception and Planning to deeply analyze system-level workloads and unlock massive performance gains through hardware-aware compute optimizations.
• Drive systemic performance excellence by designing advanced profiling and benchmarking infrastructure to identify, triage, and eliminate bottlenecks across the entire end-to-end ML software stack.

You have:

• B.S. or M.S. in CS, EE, Deep Learning or a related field.
• People management experience, with a proven track record of recruiting, mentoring, and guiding high-performing teams of senior engineers.
• 8+ years of professional software engineering experience architecting, building, and scaling complex ML systems and infrastructure.
• Strong production programming expertise.
• Proven track record of optimizing ML software to maximize the performance of hardware accelerators (e.g., GPUs, TPUs, or custom silicon).
• Hands-on experience developing distributed backend systems that are low-latency, highly concurrent, and fault-tolerant at scale.

We prefer: 

• PhD in CS, EE, Deep Learning or a related field.
• Deep expertise in modifying and extending ML software stacks, including compilers, runtimes, or inference engines (e.g., OpenXLA/PjRT, TensorRT, ONNX Runtime, TVM).
• Strong background in building and scaling LLM serving systems, leveraging advanced distributed inference and performance optimization techniques.
• Deep expertise in edge computing and automotive ML deployment, navigating strict power, thermal, and real-time latency constraints to optimize and deploy mission-critical models on resource-constrained embedded hardware.

The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. 

Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.