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About the Role
As a Staff Software Engineer for the Data Enrichment Platform, you will own the technical vision for systems that enable Wayve’s teams to deploy, run, evaluate, and continually improve machine-learning models. You will shape a platform that processes petabytes of driving data, operates across large GPU fleets, and supports dozens of model engineers. You will create dependable, self-service capabilities that improve model quality while reducing cost and operational effort.
Responsibilities
- Define the technical vision and architecture for Wayve’s end-to-end Data Enrichment Platform.
- Lead the development of backend services, APIs, model-execution workflows, versioned outputs, annotation capabilities, and dataset catalogues.
- Build scalable and reliable systems for large-scale inference, automated evaluation, model monitoring, active learning, and retraining.
- Partner with platform teams to address data locality, scheduling, capacity, backpressure, failure recovery, observability, and cost across petabyte-scale datasets and large GPU fleets.
- Make effective build, buy, reuse, integrate, consolidate, and replace decisions as Wayve’s requirements and the technology landscape evolve.
- Establish clear technical boundaries and working relationships across AI Platform, infrastructure, compute, storage, data, and model-engineering teams.
- Remain hands-on while leading cross-team delivery, mentoring engineers, and driving the adoption of reusable, self-service platform capabilities.
- Define measurable service levels and success metrics covering platform reliability, throughput, cost, automation, and adoption.
Requirements
- Experience owning the long-term technical direction and measurable outcomes of a complex, multi-system platform or capability.
- Strong production software-engineering experience in Python, including building maintainable, tested, and observable backend services, APIs, and data-processing systems.
- Deep experience designing and operating large-scale distributed systems for data-intensive or compute-intensive workloads.
- A track record of productising internal platforms for broad adoption, with a focus on reliability, usability, and self-service.
- Excellent architecture and technology judgement, combined with the ability to remain hands-on and lead delivery across organisational boundaries.
- Strong communication and influencing skills, with experience mentoring senior engineers and aligning multiple technical teams.
Skills
- Python
- Flyte
- Airflow
- Dagster
- Argo
- Spark
- Databricks
- Ray
- Kubernetes
- GPU infrastructure
- Cloud architecture
Experience Level
- Staff