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Wayve

End-to-end autonomous-driving software for vehicles

Machine Learning Software Engineer

Backend EngineeringUpdated on Nov 9
Full-timeSunnyvale, CA, USA

About the Job

Requirements

  • Strong software engineering skills and experience building high-quality, maintainable software.
  • Experience building and maintaining machine learning pipelines or infrastructure, including data ingestion, training, evaluation, or experiment workflows.
  • Experience designing reliable, reusable software systems and abstractions that can evolve as requirements change.
  • Hands-on experience with modern machine learning frameworks and a good understanding of machine learning training and experimentation workflows.
  • Strong debugging skills and the ability to investigate problems across complex machine learning systems.
  • Experience with software testing, code quality, and engineering practices for maintaining a healthy shared codebase.
  • Experience working with large datasets, large models, or other computationally demanding machine learning workloads.
  • Ability to collaborate closely with researchers and engineers and translate research requirements into practical software systems.

Responsibilities

  • Design and maintain scalable pipelines for data ingestion, model training, evaluation, and experimentation.
  • Build reusable interfaces and shared tooling across data, models, training, evaluation, and downstream robotics workflows.
  • Partner with researchers to translate evolving model and experiment needs into practical software systems.
  • Diagnose and resolve performance, reliability, and usability bottlenecks across large-scale machine learning workloads.
  • Raise the quality of the shared codebase through strong architecture, testing, documentation, and engineering standards.
  • Support distributed training and data processing across large video, language, and robot-interaction datasets.
  • Design, build, and maintain scalable machine learning pipelines for data ingestion, model training, evaluation, and related research workflows.
  • Build software systems that allow machine learning workloads to scale to larger datasets, larger models, and more experiments.
  • Develop clean and reusable interfaces between data, models, training, evaluation, and downstream robotics workflows.
  • Own and improve the health of the MEGA codebase, including software architecture, testing, reliability, maintainability, and engineering standards.
  • Identify and resolve performance, reliability, and usability bottlenecks across machine learning workflows.
  • Build and maintain infrastructure that supports multiple researchers and machine learning projects without unnecessarily slowing down iteration.
  • Work closely with researchers to understand the requirements of new models and experiments, and translate those requirements into practical software solutions.
  • Build and use distributed training and data-processing pipelines for large models and large multimodal datasets.

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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