Key Notes
AI Libraries supplies Python platforms and tools for Wayve’s ML engineers and researchers, covering data loading, distributed training, inference, checkpointing and evaluation. Essential skills include Python, software architecture, cloud environments, concurrent or distributed computing and familiarity with ML frameworks. The work centres on reusable engineering infrastructure and multimodal sensor pipelines, with reliability and usability for technical users central to delivery.
What You'll Work On
- Design, build and maintain scalable Python libraries and tools used by Wayve’s ML engineers and researchers.
- Develop reusable abstractions for data loading, distributed training, inference, checkpointing and model-evaluation workflows.
- Support model training across large GPU clusters and cloud infrastructure while improving reliability, observability and maintainability.
- Optimise data and training pipelines for multi-modal camera, radar, lidar and other sensor data.
- Work with ML teams to refine requirements and evolve Wayve’s AI platform through documented, adoptable engineering tools.
Why This Role Matters
- Wayve’s ML engineers and researchers depend on the AI Libraries platform for training and evaluation workflows, so reusable software abstractions determine how consistently those workflows can operate at scale.
- Large-scale autonomous-driving model development uses GPU infrastructure and multi-modal sensor pipelines, making platform reliability and pipeline performance material to how efficiently teams can train and evaluate models.
What They Are Looking For
- Technical: Strong Python programming experience is essential.
- Experience: Proven experience designing, building and maintaining software systems from concept through delivery is essential.
- Technical: Strong software architecture and system-design skills, including tools, platforms or libraries for technical users, are essential.
- Technical: Cloud experience, ideally Azure, and experience with concurrent, parallel or distributed computing are essential.
- Technical: Familiarity with ML frameworks such as PyTorch, TensorFlow or PyTorch Lightning is essential.
- Preferred: Large GPU clusters, DDP or FSDP, observability tooling, workflow orchestration, Docker, Kubernetes, Terraform or ML-system profiling are desirable.