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Wayve

End-to-end autonomous-driving software for vehicles

Staff Software Engineer - Data Enrichment Platform

Backend EngineeringUpdated on Nov 9
Full-timeLondon, UK

About the Job

Requirements

  • You have owned the long-term technical direction of a complex, multi-system platform or capability and can demonstrate the measurable outcomes of your decisions.
  • You have strong production software-engineering experience in Python, building maintainable, tested and observable backend services, APIs and data-processing systems.
  • You have deep experience designing and operating large-scale distributed systems for data-intensive or compute-intensive workloads.
  • You have a track record of turning internal platforms into widely adopted, reliable and usable self-service products.
  • You bring excellent architecture and technology judgement, balancing immediate delivery needs with long-term platform direction.
  • You remain hands-on while leading delivery across organisational boundaries, influencing technical decisions and aligning teams around clear ownership and shared outcomes.
  • You communicate complex ideas clearly and have experience mentoring senior engineers and strengthening the technical capabilities of those around you.
  • You have people leadership experience, such as managing or technically leading a small team of 2-3 engineers, setting direction, guiding delivery and supporting their growth and performance.

Responsibilities

  • Own the technical vision and architecture for the end-to-end Data Enrichment Platform, translating user needs into a clear technical direction.
  • Lead the design, development and operation of backend services, APIs, model-execution workflows, versioned outputs, annotation capabilities and dataset catalogues.
  • Make architectural decisions for petabyte-scale data and large GPU fleets, addressing data locality, scheduling, capacity, backpressure, failure recovery, observability and cost.
  • Evaluate when to build, buy, reuse, integrate, consolidate or replace technologies as needs and the technology landscape evolve.
  • Establish clear technical boundaries and ownership across model engineering, AI Platform, infrastructure, compute, storage and data teams.
  • Remain hands-on while leading cross-team delivery, mentoring senior engineers and raising standards for maintainable, tested and observable software.
  • Lead and develop a small team of engineers, providing technical direction, coaching and regular feedback while setting priorities and supporting their career growth.
  • Drive adoption of reusable, self-service capabilities so teams can onboard and operate workflows independently.
  • Define measurable service levels and success metrics covering reliability, throughput, cost, automation and adoption.
  • Build a scalable platform for large-scale inference, automated evaluation, model monitoring, active learning and retraining.
  • Build self-service backend services and APIs that enable dozens of model engineers to deploy and operate repeatable workflows independently.
  • Build reproducible dataset selection, versioned outputs, annotation workflows and dataset catalogues with clear metadata, provenance and lineage.
  • Build reliable model-execution workflows operating across petabytes of driving data and large GPU fleets, scaling towards thousands of GPUs.
  • Build monitoring and operational tooling that make model quality, platform reliability, throughput, cost, runtime and workflow status visible.
  • Develop the foundations for continuous evaluation, drift detection and automated model-improvement loops across robotics and computer-vision workloads.
  • Deliver architectural improvements that reduce inference costs, duplicate compute and manual operational effort.

Desired Qualifications

  • Experience with workflow orchestration, batch inference, machine-learning platforms, model deployment or registries; relevant technologies include Flyte, Airflow, Dagster, Argo, Spark, Databricks, Ray, Kubernetes and GPU infrastructure.
  • Experience with annotation or dataset-catalogue platforms, including provenance, versioning, lineage and quality control.
  • Experience with production robotics or computer-vision platforms, including annotation, inference, evaluation and model monitoring. Computer-vision research expertise is not required.

About the Company

Wayve

End-to-end autonomous-driving software for vehicles

Wayve provides end-to-end autonomous-driving software for automakers and fleets that runs entirely on onboard compute and sensors, without relying on HD maps or city-by-city engineering. The system uses foundation models capable of zero-shot driving and has demonstrated ability to drive across 500+ cities in Europe, North America, and Japan. Unlike traditional AV stacks that depend on mapped data and per-city customization, Wayve aims to generalize driving with its onboard AI across diverse environments. Its goal is to scale commercial robotaxi trials from 2026 and bring supervised autonomy to consumer vehicles from 2027, backed by substantial funding partnerships to support rapid deployment.

Company size
1,001-5,000
Company stage
Series D
Total funding
$2.6B
Headquarters
London, United Kingdom
Founded
2017

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