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Wayve is building the AI platform for autonomous driving, pioneering an end-to-end approach that enables vehicles to learn directly from real-world experience. The Data Enrichment Platform team builds the orchestration infrastructure that enables Wayve's teams to deploy, run, evaluate and continually improve machine-learning models at scale.
In this role, you will design, build and operate backend services, APIs and data-processing workflows for the Data Enrichment Platform. Your day-to-day work includes developing model-execution workflows, versioned outputs, annotation capabilities and dataset-catalogue features. You'll improve the scalability and reliability of systems handling large-scale inference, automated evaluation and model monitoring across petabyte-scale data and GPU-backed workloads. You'll investigate and resolve reliability, performance and cost issues, contribute to designs covering data locality, scheduling, capacity, backpressure, failure recovery and observability, and collaborate across model engineering, AI Platform, infrastructure, compute, storage and data teams.
Key deliverables include a scalable platform for large-scale inference, automated evaluation, model monitoring, active learning and retraining; backend services and APIs enabling model engineers to deploy and operate repeatable workflows independently; reproducible dataset selection with 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; and monitoring and operational tooling that make platform reliability, throughput, cost and workflow status visible.
Wayve operates a hybrid working model combining in-person collaboration in dedicated office spaces with focused remote work. The company has main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg.
REQUIREMENTS:
- Strong production software-engineering experience in Python, including building maintainable, tested and observable backend services, APIs or data-processing systems
- Experience designing, building or operating distributed systems for data-intensive or compute-intensive workloads
- Demonstrated ability to deliver reliable software from design through deployment and production operation
- Good system-design fundamentals and ability to make sound technical trade-offs within a complex platform
- Comfortable investigating unfamiliar systems and diagnosing reliability, performance or operational issues
- Clear communication, effective cross-team collaboration and meaningful ownership of production components
- Advantageous: experience with Flyte, Airflow, Dagster, Argo, Spark, Databricks, Ray, Kubernetes, GPU infrastructure or ML-platform systems
- Desirable: experience with production robotics or computer-vision platforms including annotation, inference, evaluation and model monitoring (computer-vision research expertise not required)
