8 hours ago
London, United KingdomStaff+
Responsibilities
- Define the technical vision and architecture for Wayve’s end-to-end Data Enrichment Platform.
- Lead development of backend services, APIs, model-execution workflows, versioned outputs, annotation capabilities, and dataset catalogues.
- Build scalable systems for large-scale inference, automated evaluation, model monitoring, active learning, and retraining.
- Address data locality, scheduling, capacity, backpressure, failure recovery, observability, and cost across petabyte-scale datasets and large GPU fleets.
- Make build, buy, reuse, integrate, consolidate, and replace decisions as requirements and technology evolve.
- Establish 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 adoption of reusable self-service platform capabilities.
- Define measurable service levels and success metrics for 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 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.
- Track record of productising internal platforms for broad adoption with a focus on reliability, usability, and self-service.
- Excellent architecture and technology judgement, with the ability to remain hands-on and lead delivery across organisational boundaries.
- Strong communication and influencing skills, including experience mentoring senior engineers and aligning multiple technical teams.
- Experience with workflow orchestration technologies such as Flyte, Airflow, Dagster, or Argo is desirable.
- Experience with large-scale batch inference or ML-platform systems, model deployment, and model registries is desirable.
- Knowledge of Spark, Databricks, Ray, or comparable distributed-compute technologies is desirable.
- Experience with annotation or dataset-catalogue platforms, including metadata, provenance, versioning, lineage, and quality control, is desirable.
- Experience with Kubernetes, GPU infrastructure, and cost-aware cloud architecture is desirable.
- Experience implementing continuous evaluation, model-quality monitoring, active learning, or automated retraining workflows is desirable.
- Experience designing or operating production platforms for robotics or computer-vision workloads is desirable; computer-vision research expertise is not required.
Benefits
- The company supports accommodations or adjustments for an inclusive interview experience.
- The role is based at Wayve; no specific remote, hybrid, or office schedule is stated.
Tech Stack
Categories
About Wayve
Wayve builds end-to-end autonomous driving software—the vehicle-agnostic Wayve AI Driver—that runs on onboard compute and native sensors, licensed to automakers and fleet operators. Its platform spans ADAS and higher autonomy (L2+/L3 to robotaxi) and is designed to generalize across vehicle types and geographies. Founded in 2017 and headquartered in London, it tests its models across Europe, North America, and Japan, with a U.S. base in Sunnyvale, CA.