Key Notes
Model-development applications connect data, training, experiment scheduling, evaluation and on-road testing in this full-stack role. Strong production web engineering, API and data design, distributed-system fundamentals and end-to-end ownership are essential. The engineer combines hands-on delivery with design discussions, reusable patterns, code reviews and mentoring, working closely with Product, Research, Operations and Design colleagues.
What You'll Work On
- Build production applications that organise model-development workflows across data, training, experiment scheduling, evaluation and on-road vehicle testing.
- Develop and operate web interfaces, APIs, backend services and data models with production security, reliability and observability.
- Own significant features from user discovery and technical design through implementation, deployment, support and continuous improvement.
- Shape platform system design, reusable patterns and architectural trade-offs across scalability, maintainability, security and sustainable delivery speed.
- Mentor engineers and coordinate Product, Research, Operations, Design and engineering stakeholders around practical model-development platform solutions.
Why This Role Matters
- Wayve’s researchers use integrated scheduling, evaluation and on-road-test applications to manage complex model-development workflows through a coherent set of services.
- Testing, monitoring and observability expose failures in production APIs and backend services, giving Wayve’s platform team evidence to address reliability as model-development workloads grow.
What They Are Looking For
- Experience: Strong full-stack experience building and operating production web applications across frontend and backend systems is essential.
- Technical: React, TypeScript, Python, Flask or FastAPI, or comparable modern technologies, are sought as relevant experience.
- Technical: System design, API design, data modelling, asynchronous workflows, automated testing and distributed-system fundamentals are essential.
- Experience: End-to-end ownership from problem definition through deployment and production operation is essential.
- Stakeholder: Technical leadership, mentorship and clear collaboration with product, research, operations and engineering stakeholders are essential.
- Preferred: Developer or ML platforms, MLOps, workflow orchestration, observability, security governance, robotics or simulation experience is desirable.