About the job
As Principal Engineer for the Model Development Platform, you'll own the end-to-end architecture behind Wayve's AI model lifecycle, from data ingestion and training to experiment scheduling and on-road testing. Working at the intersection of AI research, large-scale distributed systems, and robotic operations, you'll keep the platform reliable, scalable, and coherent so our researchers and engineers can iterate fast and deploy autonomous driving models safely.
Responsibilities
Design and evolve the platform's overall architecture for reliability, observability, and scalability
Unify the platform across disciplines, from front-end UIs and distributed training to Spark data pipelines and optimization-based experiment scheduling
Dive into the hardest challenges across subteams, lead architectural reviews, and propose pragmatic solutions that balance innovation with operational simplicity
Build systems that optimize how models are tested in simulation and on-road, using techniques like linear programming and heuristic optimization
Architect pipelines that ingest, transform, and enrich petabytes of fleet sensor data, and drive efficient compute use across GPU, CPU, cloud, and edge for both prototyping and large-scale training
Partner with Product, Research, and Operations to align architecture with user needs and co-own the platform's long-term roadmap.
Qualifications
Minimum
10+ years of experience designing and building large-scale distributed systems, ML/AI infrastructure, full stack web application, or developer platforms, including at least 3 years as a staff or principal-level engineer
Proven ability to design systems spanning web platforms, ML pipelines, and large-scale compute orchestration (e.g., Spark, Ray, Kubernetes, Airflow, MLflow)
Experience driving platform reliability improvements, defining SLAs/SLOs, and building self-healing and observable systems that operate at “four nines” availability or better
Deep understanding of distributed computing, workflow orchestration, data modeling, and API design, with the ability to write and review production-quality code
Excellent communication and cross-functional collaboration skills; ability to guide engineers, managers, and researchers toward unified technical direction
Demonstrated success in mentoring engineers across levels and cultivating a culture of engineering excellence
Preferred
Experience applying algorithmic or mathematical optimization (e.g., linear programming, graph algorithms) to operational or scheduling problems
Familiarity with end-to-end model lifecycle tooling, from data ingestion and training CI to model artifact tracking and evaluation workflows
Prior exposure to autonomous systems, robotics, or other safety-critical domains
Experience with modern web frameworks (e.g., React, Flask, FastAPI) and how they integrate into backend systems