Lili Jiang
Director Of Engineering at Waymo
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolLili Jiang is a seasoned engineering leader with 12 years of experience building and evaluating large-scale ML systems for autonomous driving and consumer products. Currently Director of Engineering at Waymo, she has led teams that compressed ML and physics-based evaluation cycles from months to hours and now oversees evaluation of large-scale models powering autonomy. Previously she was Head of Data Science at Quora and a founding AI engineer at an early-stage startup, giving her a rare blend of hands-on modeling, product judgment, and org-scale leadership. Her background in physics (Harvard) and a first-author publication reflect deep analytical rigor that she applies to production ML problems. Lili also contributes practical tooling and visualizations—e.g., an interactive gradient descent visualization that highlights hyperparameter effects—demonstrating curiosity about explainability and developer UX. She is based in the San Francisco Bay Area and known for turning complex research into reliable, ship-ready systems.
12 years of coding experience
9 years of employment as a software developer
Bachelor's degree Physics with minor in Computer Science, Bachelor's degree Physics with minor in Computer Science at Harvard University
English, Chinese
Github Skills (9)
data-visualizations10
memory-management10
data-visualization10
data-visualisation10
c-language10
cprogramming-language10
qt10
user-interface9
algorithms7
Programming languages (5)
Github contributions (5)
lilipads/gradient_descent_viz
May 2020 - Jun 2020
interactive visualization of 5 popular gradient descent methods with step-by-step illustration and hyperparameter tuning UI
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:1 review, 112 commits, 1 PR in 19 days
Contributions summary:Lili refactored and improved the visualization of gradient descent algorithms. They converted the code to use unique pointers for memory management, initialized graph, point and surface. They also added gradient colors, a learning rate control, and a restart button, as well as implemented features such as path visualization. They contributed to a more interactive and feature-rich visualization of gradient descent methods.
lilipads/machine_learning_paper_reading_notes
Apr 2020 - Dec 2021
Contributions:36 commits, 35 pushes, 1 branch in 1 year 8 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.