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.
🏢 About Wayve
Wayve is an AI company focused on developing autonomous driving technology.
🎯 The Role
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.
✅ Key Responsibilities
System architecture & reliability - Design and evolve the platform's overall architecture for reliability, observability, and scalability.
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.
Hands-on problem solving - Dive into the hardest challenges across subteams, lead architectural reviews, and propose pragmatic solutions.
Experimentation & scheduling systems - Build systems that optimize how models are tested in simulation and on-road.
Data & compute infrastructure - Architect pipelines that ingest, transform, and enrich petabytes of fleet sensor data.
Strategic collaboration - Partner with Product, Research, and Operations to align architecture with user needs.
📌 Required Qualifications
Technical Leadership at Scale – 10+ years of experience designing and building large-scale distributed systems, ML/AI infrastructure, full stack web application, or developer platforms.
Architectural Depth & Breadth – Proven ability to design systems spanning web platforms, ML pipelines, and large-scale compute orchestration.
Reliability and performance – Experience driving platform reliability improvements, defining SLAs/SLOs, and building self-healing and observable systems.
Hands-On Systems Design – Deep understanding of distributed computing, workflow orchestration, data modeling, and API design.
Collaborative Influence – Excellent communication and cross-functional collaboration skills.
Mentorship & Culture – Demonstrated success in mentoring engineers and cultivating a culture of engineering excellence.
ML Ops & Experimentation Systems – Familiarity with end-to-end model lifecycle tooling.
Domain Experience – Prior exposure to autonomous systems, robotics, or other safety-critical domains.
Full-Stack Fluency – Experience with modern web frameworks.
Data Governance – Understanding of data privacy, compliance, and secure handling practices.
🎁 Benefits
Competitive salary ranging from $295,500 to $335,300, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.
