Software Engineer, AI Platforms & Libraries

Wayve; Greater London; On-site; GBP 90,000 - 135,000; Full time

Job summary

Wayve in London is seeking a Software Engineer for the AI Libraries team. You will build and maintain platforms, libraries, and tools that enable Wayve’s ML engineers and researchers to train, evaluate, and scale models efficiently.

You’ll design stable, modular systems, collaborate with ML teams, and improve the reliability and performance of training infrastructure at scale. This role focuses on software engineering over ML modelling, delivering practical, scalable solutions.

Qualifications

• Proficient Python programming with ability to design and deliver software systems.
• Strong software architecture skills with experience building reusable tools or libraries.
• Experience with cloud environments, preferably Azure.
• Experience with concurrent, parallel, or distributed computing.
• Familiarity with ML frameworks such as PyTorch or TensorFlow.
• Ability to work with technical stakeholders to refine requirements and deliver scalable solutions.

Responsibilities

• Design, build, and maintain scalable Python libraries and tools for ML engineers and researchers.
• Develop robust abstractions for data loading, distributed training, inference, and evaluation workflows.
• Support training at scale across large GPU clusters and cloud infrastructure.
• Collaborate with ML teams to understand needs and create reliable, well-documented tools.
• Improve software architecture, testing, monitoring, and maintainability of ML systems.

Skills

Python

Software architecture

Distributed computing

ML frameworks

Stakeholder collaboration

Tools

Azure

Docker

Kubernetes

Prometheus

Grafana

Datadog

OpenTelemetry

Job description

Wayve in London is seeking a Software Engineer for the AI Libraries team. You will build and maintain platforms, libraries, and tools that enable Wayve’s ML engineers and researchers to train, evaluate, and scale models efficiently.

You’ll design stable, modular systems, collaborate with ML teams, and improve the reliability and performance of training infrastructure at scale. This role focuses on software engineering over ML modelling, delivering practical, scalable solutions.