Technical Lead Manager, Machine Learning Runtime & Serving

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.