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Software Engineer (AI Platform)

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Onsite

Software Engineer (AI Platform)

Wayve

Software Engineer (AI Platform)

Skills

DockerPythonPyTorchTensorFlowDatadogGrafanaMachine LearningAIPrometheusKubernetesApache AirflowTerraformAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

London, UK

Work mode:

Onsite

Published on:

30 August 2026

What they need


  • We’re looking for a Software 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
  • 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

Benefits

  • Private healthcare: Choose our optional health insurance for comprehensive coverage for you and your family.
  • Paid time off: Paid vacation plus public holidays and additional leave programs, ensuring you have time to unwind.
  • Mental health resources: Through Spill, you can access therapy and mental health support.
  • Community and socials: Join clubs or attend team socials to connect over hobbies, sports, or just for fun.
  • Competitive compensation: Our compensation package includes cash and equity, making you a true partner in our success.
  • Learning and development: Budgets for books, courses, and company-wide training to support your continuous growth.- 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
  • Familiarity with ML frameworks such as PyTorch, TensorFlow, or PyTorch Lightning
  • 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
  • Strong Python programming experience.Proven experience designing, building, and maintaining software systems from concept through to delivery
  • Experience with concurrent, parallel, or distributed computing
  • Ability to work closely with technical stakeholders to refine requirements and deliver practical, scalable solutions
  • 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

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