Staff Software Engineer, Data Enrichment Platform

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 🛠️ About our Data Enrichment Platform Team Our Enrichment Orchestration team builds the platform that enables Wayve’s teams to deploy, run, evaluate and continually improve machine-learning models. Processing petabytes of driving data across large GPU fleets, the team creates dependable, self-service capabilities that help model engineers understand model performance, improve quality and reduce operational effort and cost. As our Staff Software Engineer, you’ll own the platform’s technical vision and long-term direction. You’ll combine hands-on engineering with cross-team technical leadership, shaping how model engineering, infrastructure, compute and data capabilities come together to accelerate model development. 🧠 Your day-to-day • Own the technical vision and architecture for the end-to-end Data Enrichment Platform, translating user needs into a clear technical direction. • Lead the design, development and operation of backend services, APIs, model-execution workflows, versioned outputs, annotation capabilities and dataset catalogues. • Make architectural decisions for petabyte-scale data and large GPU fleets, addressing data locality, scheduling, capacity, backpressure, failure recovery, observability and cost. • Evaluate when to build, buy, reuse, integrate, consolidate or replace technologies as Wayve’s needs and the technology landscape evolve. • Establish clear technical boundaries and ownership across model engineering, AI Platform, infrastructure, compute, storage and data teams. • Remain hands-on while leading cross-team delivery, mentoring senior engineers and raising standards for maintainable, tested and observable software. • Drive adoption of reusable, self-service capabilities so teams can onboard and operate workflows independently. • Define measurable service levels and success metrics covering reliability, throughput, cost, automation and adoption. 🧩 What you’ll be working on • A scalable platform for large-scale inference, automated evaluation, model monitoring, active learning and retraining. • Self-service backend services and APIs that enable dozens of model engineers to deploy and operate repeatable workflows independently. • Reproducible dataset selection, versioned outputs, annotation workflows and dataset catalogues with clear metadata, provenance and lineage. • Reliable model-execution workflows operating across petabytes of driving data and large GPU fleets, scaling towards thousands of GPUs. • Monitoring and operational tooling that make model quality, platform reliability, throughput, cost, runtime and workflow status visible. • The foundations for continuous evaluation, drift detection and automated model-improvement loops across robotics and computer-vision workloads. • Architectural improvements that reduce inference costs, duplicate compute and manual operational effort. 🙌 You should apply if • You have owned the long-term technical direction of a complex, multi-system platform or capability and can demonstrate the measurable outcomes of your decisions. • You have strong production software-engineering experience in Python, building maintainable, tested and observable backend services, APIs and data-processing systems. • You have deep experience designing and operating large-scale distributed systems for data-intensive or compute-intensive workloads. • You have a track record of turning internal platforms into widely adopted, reliable and usable sel