Waymo
Staff Machine Learning Engineer, Runtime & Optimization
Waymo, Bellevue, Washington, us, 98009
Overview
Staff Machine Learning Engineer, Runtime & Optimization Join to apply for the
Staff Machine Learning Engineer, Runtime & Optimization
role at
Waymo . 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 built the Waymo Driver—the World's Most Experienced Driver—to improve mobility access while reducing traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can be applied to a range of vehicle platforms and product use cases. Waymo Driver has provided over ten million rider-only trips and has driven over 100 million miles on public roads plus tens of billions in simulation across 15+ states in the U.S. 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), to improve inference speed and reduce inference memory on modern GPU and TPU architectures Develop new neural model architectures (e.g., sparse architectures), decoding strategies (e.g., speculative decoding) to improve 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 develop 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
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Staff Machine Learning Engineer, Runtime & Optimization Join to apply for the
Staff Machine Learning Engineer, Runtime & Optimization
role at
Waymo . 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 built the Waymo Driver—the World's Most Experienced Driver—to improve mobility access while reducing traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can be applied to a range of vehicle platforms and product use cases. Waymo Driver has provided over ten million rider-only trips and has driven over 100 million miles on public roads plus tens of billions in simulation across 15+ states in the U.S. 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), to improve inference speed and reduce inference memory on modern GPU and TPU architectures Develop new neural model architectures (e.g., sparse architectures), decoding strategies (e.g., speculative decoding) to improve 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 develop 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
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