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Wayve is building the leading AI platform for autonomous driving, pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience. The Data and Orchestration team in London builds the platforms behind Wayve's data engineering, orchestration, and inference workflows, handling high-volume data ingestion, processing, and transformation from external vendors, vehicle fleets, and customer programmes.
In this role, you'll help evolve Wayve's data pipeline platform by revamping the existing Harness system and building a new DAG-based orchestration system for the fleet's next-generation data volumes. You'll design, build, and maintain scalable data pipeline and orchestration infrastructure supporting hundreds of parallel pipeline steps with efficient resource prioritization, correct outputs, and no conflicts.
Your responsibilities include:
- Designing and evolving scalable data pipeline and orchestration infrastructure for autonomous driving data platforms
- Revamping existing systems and developing new DAG-based workflow platforms
- Building tooling and libraries that make data ingestion, processing, inference, and evaluation workflows simple, reliable, and consistent across teams
- Supporting batch inference and performance optimization workflows for cost-effective, scalable evaluations
- Improving platform reliability, quality, and usability, including incident response workflows aligned to business-defined SLOs
- Working closely with partner teams to understand pipeline requirements and translate them into scalable technical solutions
Wayve operates a hybrid working model combining in-person collaboration in dedicated office spaces with focused remote work. The company has main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg.
REQUIREMENTS:
Must-have:
- Strong software engineering fundamentals with experience designing, building, and maintaining production-grade systems
- Solid Python programming skills
- Experience working with systems at scale, ideally involving distributed systems, large data volumes, or high-throughput processing
- Understanding of software engineering best practices including unit testing, integration testing, test-driven development, and data-driven development
- Familiarity with distributed event streaming platforms such as Apache Kafka
- Understanding of database principles and consistency models (eventual consistency, strong consistency, distributed data systems)
- Familiarity with traditional databases or data warehouses such as Postgres, ClickHouse, or column-oriented databases
- Ability to work well with platform users and partner teams, translating requirements into scalable technical solutions
Nice-to-have:
- Experience in data engineering, data platforms, workflow orchestration, or pipeline infrastructure
- Experience with machine learning infrastructure, inference systems, or ML performance optimization
- Exposure to large-scale batch processing, compute scheduling, or resource prioritization
- Experience with tools or frameworks such as Ray, Flyte, or Spark
- Experience building shared libraries, developer tooling, or internal platforms used across multiple teams
