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 self-service products. You bring excellent architecture and technology judgement, balancing immediate delivery needs with long-term platform direction. You remain hands-on while leading delivery across organisational boundaries, influencing technical decisions and aligning teams around clear ownership and shared outcomes. You communicate complex ideas clearly and have experience mentoring senior engineers and strengthening the technical capabilities of those around you. Experience with workflow orchestration, batch inference, ML platforms, model deployment or registries would be an advantage. Relevant technologies include Flyte, Airflow, Dagster, Argo, Spark, Databricks, Ray, Kubernetes and GPU infrastructure. Experience with annotation or dataset-catalogue platforms, including provenance, versioning, lineage and quality control, would also be valuable. Experience with production robotics or computer-vision platforms—including annotation, inference, evaluation and model monitoring—is particularly desirable. Computer-vision research expertise is not required. 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and the impact you could make at Wayve, we encourage you to apply even if you don’t meet every requirement. More about Wayve 🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand-coded rules or pre-mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once-in-a-generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human-driven vehicles to intelligent machines. Our ambition is to make autonomy universal. Wayve’s mapless and hardware-agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2+ thr