Principal Engineer, Model Development Platform

About the role

The Principal Engineer for the Model Development Platform at Wayve will own the end-to-end architecture behind Wayve's AI model lifecycle, from data ingestion and training to experiment scheduling and on-road testing. This role involves working at the intersection of AI research, large-scale distributed systems, and robotic operations, ensuring the platform is reliable, scalable, and coherent.

Responsibilities

• 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.

• 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.

• 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 to balance hardware, safety, and research priorities while improving throughput and turnaround.

• 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.

Requirements

• 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.

Desirable

• 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.

• Full-Stack Fluency in modern web frameworks (e.g., React, Flask, FastAPI) and how they integrate into backend systems.

• Understanding of data privacy, compliance, and secure handling practices for large-scale sensor data.

What You'll Own

System architecture & reliability
Cross-domain technical leadership
Hands-on problem solving
Experimentation & scheduling systems
Data & compute infrastructure
Strategic collaboration

Essential

• 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 and performance – 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.

Desirable

• 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.