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.