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As the Principal Engineer for the Model Development Platform at Wayve, you will own the end-to-end architecture that powers every aspect of our AI model lifecycle—from raw data ingestion to model training, experiment scheduling, and on-road testing. Sitting at the intersection of cutting-edge AI research, large-scale distributed systems, and robotic operations, you will ensure the reliability, scalability, and coherence of the systems that enable Wayve’s researchers and engineers to iterate rapidly and deploy autonomous driving models safely.
Job Responsibility:
Design and evolve the overarching architecture of the model development platform, ensuring system-wide reliability, observability, and scalability
Work across disciplines—from front-end web UIs to large-scale distributed training, from Spark-based data pipelines to experiment scheduling algorithms using linear optimization—to unify the platform’s architecture and ensure smooth interoperability between systems
Dive deep into the thorniest technical challenges faced by individual subteams, bringing your expertise in distributed systems, large-scale compute, and system design to bear
Develop and refine systems that optimize how models are tested—whether in simulation or on-road—balancing constraints like hardware availability, safety requirements, and research priorities
Architect data processing pipelines capable of ingesting, transforming, and enriching petabytes of sensor data from the global fleet
Serve as a mentor and coach for engineers across the organization—developing technical talent, improving design practices, and fostering a culture of learning and technical excellence
Partner with Product Management, Research, and Operations to align technical architecture with user needs and product vision
Requirements:
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, including at least 3 years as a staff or principal-level engineer
Architectural Depth & Breadth – Proven ability to design systems spanning web platforms, ML pipelines, and large-scale compute orchestration (e.g., Spark, Ray, Kubernetes, Airflow, MLflow)
Reliability & Performance Mindset – Experience driving platform reliability improvements, defining SLAs/SLOs, and building self-healing and observable systems that operate at “four nines” availability or better
Hands-On Systems Design – Deep understanding of distributed computing, workflow orchestration, data modeling, and API design, with the ability to write and review production-quality code
Collaborative Influence – Excellent communication and cross-functional collaboration skills
ability to guide engineers, managers, and researchers toward unified technical direction
Mentorship & Culture – Demonstrated success in mentoring engineers across levels and cultivating a culture of engineering excellence
Education – Bachelor’s degree in Computer Science, Software Engineering, or related field (advanced degree preferred, or equivalent experience)
Nice to have:
Optimization & Scheduling Expertise – Experience applying algorithmic or mathematical optimization (e.g., linear programming, graph algorithms) to operational or scheduling problems
ML Ops & Experimentation Systems – Familiarity with end-to-end model lifecycle tooling, from data ingestion and training CI to model artifact tracking and evaluation workflows
Domain Experience – Prior exposure to autonomous systems, robotics, or other safety-critical domains
Full-Stack Fluency – Experience with modern web frameworks (e.g., React, Flask, FastAPI) and how they integrate into backend systems
Data Governance – Understanding of data privacy, compliance, and secure handling practices for large-scale sensor data
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