Principal Engineer, Model Development Platform

Job description

About us Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact. Make Wayve the experience that defines your career! 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. Partnering with the Head of Model Dev Platform, you'll set and execute the technical vision, aligning infrastructure and tooling with company goals. You'll lead by example, going deep across web applications, distributed compute, ML Ops, data pipelines, and optimization algorithms, and through architecture and mentorship you'll enable teams to build platform capabilities that measurably accelerate model development and fleet learning. What you'll own System architecture & reliability - Design and evolve the platform's overall architecture for reliability, observability, and scalability. Set performance, latency, and availability targets, and drive the engineering standards to meet them. Cross-domain technical leadership - Unify the platform across disciplines, from front-end UIs and distributed training to Spark data pipelines and optimization-based experiment scheduling, ensuring systems interoperate cleanly. Hands-on problem s…