Staff Machine Learning Engineer, Model Optimization

Software Engineering builds the brains of Waymo's fully autonomous driving technology. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We think deeply and solve complex technical challenges in areas like robotics, perception, decision-making and deep learning, while collaborating with hardware and systems engineers. If you're a software engineer or researcher who's curious and passionate about Level 4 autonomous driving, we'd like to meet you.

The ML Platform team at Waymo provides a set of tools and technologies to support and automate the lifecycle of the machine learning workflow, including feature and experiment management, model development, debugging & evaluation, optimization, deployment, and monitoring. These efforts have resulted in making machine learning more accessible to teams at Waymo, including Perception, Planner, Research and Simulation, ensuring greater degrees of consistency and repeatability, and addressing the "last mile" of getting models into production and managing them once they are in place. We work hand in hand with machine learning experts in all parts of the company as well as our collaborators across Alphabet.

We are looking for a staff engineer with model optimization expertise to help us improve compute performance on our car. You'll work across the entire ML stack from models, to ML frameworks/libraries, to different HW platforms. You will collaborate with the world-class scientists and engineers in Waymo, Google, Deepmind and will be pleasantly challenged with the state-of- art-model compression technologies.

In this role you'll:

• Lead the collaboration with the world-class Waymo ML scientists in perception, planner, research and simulation. Build productive relationships and understand their models. Identify opportunities in both systems and models to make ML workloads faster.
• Lead projects from proposals, through goals and execution, to results. Lead and mentor junior engineers.
• Deep dive into the full stack of ML software stack. Analyze the ML workload performance. Apply model optimization, efficient deep learning techniques and ML software improvements.
• Collaborate on foundation models and ML System with external partners such as CoreML, Google Brain and Deepmind.

At a minimum, we'd like you to have:

• M.S. in CS, EE, Deep Learning or a related field
• 2+ years of experience as a technical lead
• 3+ years of experience on model optimization or efficient deep learning techniques
• Strong Python or C++ programming skills
• Thorough understanding of key ML system challenges and trade-offs.
• Solid experience with designing, training and debugging deep learning models to achieve the highest scores/accuracies.

It’s preferred if you have:

• PhD in CS, EE, Deep Learning or a related field.
• Proven track record on efficient deep learning and/or model optimization techniques with foundation models.
• Deep knowledge on system performance, GPU optimization or ML compiler.
• 5+ years of experience on model optimization or efficient deep learning techniques

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