- Workplace
- Remote
- Type
- Full-time
- Department
- IT
- Seniority
- Staff
- Education
- Bachelor
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.
The Waymo ML Frameworks & Efficiency team works with Research and Production teams to develop and deploy models in Perception and Planning that are core to our autonomous driving software. We help our partners by offering the best frameworks for the entire model development lifecycle and efficiency solutions for model execution. They are geared towards both scaling models and solving problems unique to ML for autonomous driving.
We are looking for engineers with ML frameworks or ML systems expertise to help us improve compute efficiency on both cloud and car. You’ll work across the entire ML stack, from deep learning model architectures, ML frameworks (e.g. JAX, XLA, etc.), to accelerator runtime. You will work closely with ML modeling teams to drive large scale and efficient model training and inference.
You Will:
- Optimize distributed ML systems for high performance on TPUs and GPUs clusters, and applying SPMD, MPMD, FSDP, etc techniques to scale our model training.
- Improve accelerator FLOPS efficiency of ML workload, including improving compiler optimizations (e.g. XLA), authoring low-level kernels (e.g. Pallas, Triton, etc.) and enabling low-precision computation.
- Develop new neural model architectures (e.g., sparse architectures), decoding strategies (e.g., speculative decoding), etc. for improving training/inference performance on modern TPU and GPU architectures.
- Evaluate and integrate open source community and Google SOTA technologies to enhance the performance and scalability of ML workloads.
- Promote best practices for distributed systems architecture and contribute to technical leadership within the team.
You Have:
- B.S. in Computer Science, Math, or 8+ years equivalent real-world experience.
- Proficient in distributed systems design with an understanding of ML efficiency.
- Experience with ML frameworks, including TensorFlow, JAX, XLA.
- Solid programming skills in Python and C++.
- Practical familiarity with profiling tools to uncover performance bottlenecks.
We Prefer:
- MS in Computer Science, Math
- Familiarity with ML frameworks like Pallas and Triton
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.
