Principal Engineer Model Development Platform
Wayve is a London-headquartered embodied-AI company developing and licensing mapless, vehicle-agnostic driving software for assisted, automated, and robotaxi applications.
About Wayve
Wayve Technologies Ltd. develops the Wayve AI Driver, an end-to-end, data-trained software platform that runs on onboard vehicle compute and native sensors. It is designed for OEM integration across L1 driver assistance through L4 automated driving, without HD maps.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will own the end-to-end architecture for the AI model lifecycle, spanning data ingestion, training, experiment scheduling, and on-road testing. You will set technical direction, improve reliability and scalability, solve complex cross-domain problems, and mentor engineers.
Requirements
- 10+ years designing and building large-scale distributed systems, ML or AI infrastructure, full-stack web applications, or developer platforms
- At least 3 years as a staff or principal-level engineer
- Experience designing web platforms, ML pipelines, and large-scale compute orchestration
- Experience with Spark, Ray, Kubernetes, Airflow, or MLflow
- Experience improving platform reliability and defining SLAs and SLOs
- Deep understanding of distributed computing, workflow orchestration, data modeling, and API design
- Ability to write and review production-quality code
- Communication, cross-functional collaboration, and mentorship skills
- Experience with optimization or scheduling algorithms
- Familiarity with model lifecycle tooling
- Experience with autonomous systems, robotics, or safety-critical domains
- Experience with React, Flask, or FastAPI
- Knowledge of data privacy, compliance, and secure sensor-data handling
Responsibilities
- Design and evolve platform architecture for reliability, observability, and scalability
- Set performance, latency, and availability targets and engineering standards
- Unify web applications, distributed training, data pipelines, and experiment scheduling
- Lead architectural reviews and solve complex technical challenges
- Build optimization-based systems for simulation and on-road experiment scheduling
- Architect petabyte-scale sensor-data pipelines and efficient compute infrastructure
- Align platform architecture and roadmap with Product, Research, and Operations
Benefits
- Competitive equity package
- Hybrid work arrangement
