Software Engineer, AI Libraries

Wayve; London (KY); On-site; USD 120,000 - 160,000; Full time

Job summary

Wayve is seeking a Software Engineer to join its AI Libraries team. You will build and maintain Python libraries and tools that empower ML engineers to train, evaluate, and scale models. The role emphasizes robust abstractions, reliable tooling, and scalable infrastructure across GPU clusters and cloud environments.

You will collaborate with ML teams to understand needs, improve observability, and advance Wayve’s AI platform as autonomous driving capabilities scale for real-world deployment.

Qualifications

• Strong Python programming experience and ability to design, build, and deliver software.
• Strong architecture and system design skills for scalable tools and libraries.
• Experience with cloud environments, ideally Azure.
• Experience with concurrent, parallel, or distributed computing.
• Familiarity with ML frameworks such as PyTorch, TensorFlow, or PyTorch Lightning.
• Ability to refine requirements with technical stakeholders to deliver scalable solutions.

Responsibilities

• Design, build, and maintain scalable Python libraries and tools used by ML engineers and researchers across Wayve.
• Develop robust abstractions for data loading, distributed training, inference, checkpointing, and model evaluation workflows.
• Support training at scale across large GPU clusters and cloud-based infrastructure.
• Work closely with ML teams to understand user needs and create tools that are reliable, well-documented, observable, and easy to adopt.
• Improve engineering quality across ML systems through strong software architecture, testing, monitoring, and maintainability practices.
• Optimise data and training pipelines to support multi-modal data sources, including camera, radar, lidar, and other sensor data.
• Contribute to the evolution of Wayve’s AI platform as we scale our autonomous driving capabilities.

Skills

Python programming

Software architecture

Cloud environments (Azure)

Distributed computing

ML frameworks (PyTorch, TensorFlow)

Stakeholder collaboration

Tools

Airflow

Docker

Kubernetes

Observability tools

Job description

The role

We’re looking for aSoftware Engineer to join our AI Libraries team. This team builds and maintains the platforms, libraries, and tools that enable Wayve’s ML engineers and researchers to train, evaluate, and scale models efficiently.

This is a hands‑on software engineering role focused on building stable, scalable, and modular systems that support large-scale ML development. You’ll work closely with ML teams across Wayve to understand their needs, design reusable abstractions, and improve the reliability, performance, and usability of our training infrastructure.

You’ll play a key role in maturing Wayve’s AI platform and helping bring autonomous driving technology into the hands of customers.

Key responsibilities

• Design, build, and maintain scalable Python libraries and tools used by ML engineers and researchers across Wayve.

• Develop robust abstractions for data loading, distributed training, inference, checkpointing, and model evaluation workflows.

• Support training at scale across large GPU clusters and cloud-based infrastructure.

• Work closely with ML teams to understand user needs and create tools that are reliable, well‑documented, observable, and easy to adopt.

• Improve engineering quality across ML systems through strong software architecture, testing, monitoring, and maintainability practices.

• Optimise data and training pipelines to support multi‑modal data sources, including camera, radar, lidar, and other sensor data.

• Contribute to the evolution of Wayve’s AI platform as we scale our autonomous driving capabilities.

About you

We’re looking for a strong software engineer who enjoys building high‑quality tools, platforms, and libraries for technical users. You care about clean abstractions, scalable architecture, reliability, and creating software that other engineers can depend on.

Essential skills

• Strong Python programming experience.Proven experience designing, building, and maintaining software systems from concept through to delivery.

• Strong software architecture and system design skills.Experience building tools, platforms, or libraries for internal or external users.Strong understanding of testing, observability, maintainability, and engineering best practices.

• Experience working with cloud environments, ideally Azure.

• Experience with concurrent, parallel, or distributed computing.

• Familiarity with ML frameworks such as PyTorch, TensorFlow, or PyTorch Lightning.

• Ability to work closely with technical stakeholders to refine requirements and deliver practical, scalable solutions.

Desirable skills

• Experience working with large GPU clusters or distributed training environments.

• Familiarity with distributed training techniques such as DDP or FSDP.

• Experience with observability tools such as Prometheus, Grafana, Datadog, or OpenTelemetry.

• Experience with data pipeline orchestration tools such as Airflow, Flyte, Ray, Metaflow, or Argo Workflows.

• Experience with containerisation and infrastructure tooling such as Docker, Kubernetes, or Terraform.Experience profiling or optimising ML systems, for example using NVIDIA Nsight.

• Understanding of ML workflows and researcher experience, even if you are not focused on model development.

What we’re not looking for

This is not primarily an ML modelling role. While an understanding of ML workflows is valuable, the core focus is on building reliable software, libraries, infrastructure, and tooling that enable ML teams to work effectively at scale.

Why join us?

• Work on high‑impact systems that directly support the development of autonomous driving technology.

• Help scale training and evaluation infrastructure across large GPU clusters.

• Build software used by ML engineers and researchers working at the frontier of embodied AI.

• Join a team focused on strong engineering standards, practical abstractions, and scalable platform design.

• Play a meaningful role in bringing autonomous driving technology closer to real‑world deployment.