The opportunity
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…
What you'll do
Technical Leadership: Proactively study the SOTA model architectures and optimizations from the community and Google, for World Models, Diffusion + flow matching techniques, and translate them into measurable technical deliverables in Waymo’s onboard driving stack.
Performance Analysis: Dev tooling innovation for model performance inspector in highly distributed training/inference setups, apply roofline analysis, understand the efficiency headrooms and drive work groups to deliver the optimizations and meet the system requirements.
Strong Execution: Innovate high performance optimizations and tools for various models and large-scale training/inference including on future next-gen TPUs and low-bit precision training/inference setup, and ensure all system components align towards achieving high performance and goodput goals.
Cross-Team Leadership: Guide efforts across multiple teams and organizations to ensure seamless integration of data generation, model development, and deployment pipelines.
Mentorship & Management: Act as a mentor to junior engineers, helping to grow their technical expertise and foster a culture of collaboration and engineering excellence. Manage the IC performance for a medium size team of ~10 engineers.
+ years of professional software engineering experience, with at least 5: years in machine learning infrastructure such as developing, training, deploying, and optimizing large-scale machine learning systems.
What they're looking for
- Experienced using ML accelerator profiling tools to uncover performance bottlenecks.
- Solid experience in the development and optimization of machine learning: infrastructure tools like DeepSpeed, PyTorch, TensorFlow, JAX, or similar frameworks.
- Deep understanding of state-of-the-art machine learning models and: architectures such as autoregressive and diffusion transformers and familiarity with custom-kernels for diverse h/w compute based efficiency.
- Strong leadership skills with experience navigating cross-functional teams: and providing technical leadership projects across multiple organizations.
