Principal Engineer, Data & Compute
Core
Design and guide the evolution of foundational compute and storage systems to fuel an end-to-end neural network training lifecycle for autonomous driving, managing thousands of GPUs and petabytes of data.
Role type
Principal Architect, AI Infrastructure
Builds
Global compute orchestration systems, petabyte-scale data federation layers, and cross-region GPU job execution platforms for hybrid/multi-cloud environments.
Domain
Autonomous driving, AI Infrastructure, Distributed Systems
Deliverable
infrastructure
Required skills
Large-scale distributed systems design, GPU-based cloud infrastructure, Petabyte-scale data architecture, Technical leadership, Mentorship, Multi-cloud orchestration
Preferred skills
Ray, Kubernetes, Airflow, Flyte, AI/ML job scheduling, Infrastructure-as-code, Safety-critical inference systems
Technologies
Kubernetes, Ray, Airflow, Flyte
Responsibilities
Define global compute strategy for training/inference workloads across thousands of GPUs; Design systems for fast access to high-volume sensor/simulation data; Build foundations for large-scale AI workloads in hybrid/multi-cloud environments; Advise leadership on compute investments and architecture; Mentor engineers and cultivate engineering excellence.
Seniority
Principal, strategy & mentorship