Senior Staff Software Engineer, ML Infrastructure

The Waymo ML Infrastructure team accelerates Waymo's mission by building the best ecosystem for sustainably innovating and shipping ML-powered intelligence. Our primary stakeholders are Research, Production, and Hardware teams, powering state-of-the-art models in Perception and Planning, core to our autonomous driving software. We provide best-in-class solutions for the entire model development lifecycle, collaborating closely with Google. Scale and efficiency are core tenets.

We seek an experienced Senior IC to lead the development of advanced AI infrastructure for multi-billion parameter machine learning models. Your expertise in multi-sensor integration, massive model scaling, and distributed training will be required for designing and scaling our systems. In this role -

You will:

• Provide deep technical leadership on large-scale ML model architectures, especially for autonomous vehicle models. Guide architectural decisions and technical direction for data, training, and deployment. Own large, complex systems, driving architectures that meet technical and business objectives.
• Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation and model training.
• Collaborate cross-functionally to derive performance and system-level requirements for large ML systems. Translate product/business goals into measurable technical deliverables, ensuring system component alignment.
• Guide efforts across teams and organizations to ensure seamless integration of data generation, model development, and deployment pipelines. Work at the intersection of data engineering, model development, and deployment, ensuring seamless integration and powering efficient innovation.
• Mentor junior engineers, growing their expertise and fostering a collaborative culture.

You Have:

• 10+ years of professional software engineering experience, with at least 5 years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.
• Solid experience in the development and optimization of machine learning infrastructure tools like DeepSpeed, PyTorch, TensorFlow, or similar frameworks.
• Strong expertise in distributed training techniques, including gradient sharding and optimization strategies for scaling large models across ML accelerator profiling tools to uncover performance bottlenecks.
• Able to lead software development projects using C++ or Python and deploying on cloud platforms such as GCP, AWS, or Azure.
• Deep understanding of state-of-the-art machine learning models such as autoregressive 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.
• Excellent communication skills, both verbal and written, with the ability to translate complex technical concepts for a broad audience.

We Prefer:

• A Master's or PhD in Computer Science, Engineering, or a related field is preferred.
• Preferred experience includes on device ML.

This role reports to an Engineering Manager

#Hybrid