Software Engineer, Data Enrichment Platform

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About the role

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. 🧠 Your day-to-day • Design, build and operate backend services, APIs and data-processing workflows for the Data Enrichment Platform. • Develop model-execution workflows, versioned outputs, annotation capabilities and dataset-catalogue features. • Improve the scalability and reliability of systems for large-scale inference, automated evaluation and model monitoring. • Investigate and resolve reliability, performance and cost issues across petabyte-scale data and GPU-backed workloads. • Contribute to designs covering data locality, scheduling, capacity, backpressure, failure recovery and observability. • Collaborate with model engineering, AI Platform, infrastructure, compute, storage and data teams to deliver reusable capabilities. • Write maintainable, tested and observable software while improving documentation, operational tooling and engineering standards. 🧩 What you’ll be working on • A scalable platform for large-scale inference, automated evaluation, model monitoring, active learning and retraining. • Backend services and APIs that allow 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. • Monitoring and operational tooling that make platform reliability, throughput, cost and workflow status visible. • The foundations for continuous evaluation, drift detection and automated model-improvement loops across robotics and computer-vision workloads. 🙌 You should apply if • You have strong production software-engineering experience in Python, including building maintainable, tested and observable backend services, APIs or data-processing systems. • You have experience designing, building or operating distributed systems for data-intensive or compute-intensive workloads. • You have delivered reliable software from design through deployment and production operation. • You have good system-design fundamentals and can make sound technical trade-offs within a complex platform. • You are comfortable investigating unfamiliar systems and diagnosing reliability, performance or operational issues. • You communicate clearly, collaborate effectively across teams and can take meaningful ownership of production components. • Experience with Flyte, Airflow, Dagster, Argo, Spark, Databricks, Ray, Kubernetes, GPU infrastructure or ML-platform systems would be an advantage. • Experience with production robotics or computer-vision platforms—including annotation, inference, evaluation and model monitoring—would be desirable, although computer-vision research expertise is not required. 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement.   More about Wayve: 🚀 Wayve is building the leading AI platform for auton

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