wayve - Software Engineer, Data Enrichment Platform

• 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.