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As a Staff Cloud Site Reliability Engineer, you will build and scale AI cloud platform reliability, define operational standards, and lead SRE initiatives.

As a Data Scientist at Wayve, you will generate actionable insights using causal methods and statistical frameworks to support AV product teams, focusing on complex data analysis and experimental techniques.

Lead the design and delivery of the Data Transfer Hub, managing large-scale data transfer pipelines and working directly with partners like Uber and Nissan.

The role involves building and scaling the reliability of AI cloud platforms, overseeing GPU compute infrastructure, incident response, and operational excellence while collaborating with various teams to enhance system efficiency and resilience.

Lead early-stage OEM engagements across Europe, managing RFQ/RFI and bid responses end-to-end. Coordinate product and engineering inputs, craft customer narratives and proposals, present to technical and executive stakeholders, and align internal teams to advance opportunities toward PoC and nomination.

Deliver production-ready ML model releases: train and iterate PyTorch models, evaluate and debug performance, apply optimizations (quantisation, distillation), collaborate with performance and deployment teams, and ensure models meet tight on-vehicle runtime constraints.

Optimize transformer-based ML inference for edge accelerators and GPUs. Implement and validate compiler, runtime, and kernel optimizations; profile end-to-end inference stacks; build benchmarking and regression tests; target multiple platforms (NVIDIA, Qualcomm); collaborate with model developers and cross-functional teams to deliver performant, maintainable on-vehicle ML systems and contribute to technical roadmaps.

Define and manage technical architecture for integrating Wayve's autonomous driving software with OEM/Tier1 vehicle systems. Translate product and customer needs into interface contracts, ownership boundaries, integration specifications, test requirements, and program-facing documentation. Coordinate across Product, Software Architecture, Validation, and customer teams and represent Wayve in OEM/Tier1 technical discussions to identify risks, dependencies, and validation criteria.

As a Product Feature Owner, you will oversee ADAS features from concept to production, collaborating with OEMs and engineering teams to align features with customer needs and ensure compliance with industry standards.

The Applied Scientist will develop AI systems for autonomous driving, focusing on machine learning, simulation, robotics, and real-world deployment, while advancing embodied AI technologies.