Staff Machine Learning Engineer, Optimization, San Francisco, CA

Staff Machine Learning Engineer, Optimization

New Yesterday

Waymo is an autonomous driving technology company with the mission to be the 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 One, a fully autonomous ride-hailing service, and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over one million rider-only trips, enabled by its experience autonomously driving tens of millions of miles on public roads and tens of billions in simulation across 13+ U.S. states. Waymo has successfully deployed self-driving cars in real-world environments- now, our imperative is to scale this capability. Scale is driven by large models and data, and we are moving to ever-larger models which generalize by being trained on more data. To achieve this, we're focused on optimizing model inference and training, ensuring these advancements gracefully generalize across multiple platforms.

Skills, Experience, Qualifications, If you have the right match for this opportunity, then make sure to apply today. In this role, you'll work embedded in an ML Engineering and Modeling team, working hand-in-hand to drive scale and multi-platform support of models. This role requires to follow the latest developments in efficient ML and bring those innovations to Waymo's production systems.

You will:

Optimize neural model architectures and systems for high performance on multiple GPU and TPU platforms (e.g., onboard vs simulation platform)

Optimize neural model performance and overall system performance for systems with hard real-time constraints (Waymo's onboard system)

Develop post-training algorithms (e.g., quantization), low-level optimizations (e.g., kernel optimization), etc. for improving inference speed and reducing inference memory consumption on modern GPU and TPU architectures

Develop new neural model architectures (e.g., sparse architectures), decoding strategies (e.g., speculative decoding), etc. for improving inference performance on modern GPU and TPU architectures

Optimize model training speed and efficiency for large models (often memory bound) and for fine-tuning (often i/o bound)

Collaborate with ML infra teams (inference frameworks, training frameworks), Onboard hardware and Simulation teams, and Alphabet's research teams

You must have:

Master's degree or PhD in Computer Science, Engineering, or a related technical field

3+ years of experience in software development for neural model inference or neural model training, and 1+ years experience with neural model inference and training optimization on modern GPU/TPU architectures

5+ years experience in software development for real-time systems, ideally experience with real-time systems running on device (e.g., Waymo's onboard system)

Proficiency in C++, Python, and modern deep learning toolkits like PyTorch or JAX

Passionate about low-level neural net optimization and willingness to learn new architectures and tools

Deep understanding of latency and quality tradeoffs as it applies to neural network architectures and practical experience making said tradeoffs

We prefer:

Experience in ML-driven production systems that develops models with large-scale data, training, evaluation, and deployment

Experience with developing and optimizing large-scale vision, video, or multi-modal foundation models

Familiarity with end-to-end models and their development challenges

Agility in a fast-paced environment

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. Salary Range$238,000—$302,000 USD #J-18808-Ljbffr

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Location:: San Francisco, CA
Salary:: $250
Job Type:: FullTime
Category:: Engineering

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