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Senior Data Analyst at Autotrader in Manchester builds and improves consumer-facing automotive marketplace features using SQL, Python, and analytics tools to drive data-informed product decisions.
This founding Cloud SRE role involves building and scaling the reliability foundations for Wayve's AI model development and GPU compute platforms. The engineer will define operational standards, manage incident response, and automate infrastructure to support large-scale autonomous driving AI workloads.
As a founding Staff SRE, you will build and scale the reliability foundations for Wayve's AI model development and GPU compute infrastructure. You will define operational standards, manage large-scale Kubernetes clusters, and ensure the performance of distributed systems supporting autonomous driving technology.
The Staff Machine Learning Engineer will lead the development of safety-critical, end-to-end driving models for autonomous vehicles using large-scale data and transformer-based architectures. This role involves setting technical strategy, conducting research, and collaborating across teams to improve model robustness and performance in real-world driving scenarios.
Analyzes operational design domains (ODDs) and traffic behaviors to define data needs for autonomous driving AI models, ensuring high reliability and cross-region generalization. Uses Python, SQL, and GIS to assess ODD boundaries, behavioral competencies, and MTBF performance gaps.
Build and maintain automated hardware-in-the-loop tests for Wayve’s self-driving software stack, covering embedded systems, ADAS, and AI models to ensure safety and reliability.
Build and run a B2B SaaS portal that connects Wayve’s AI autonomy platform to automaker partners, shipping backend services, APIs, and UIs while owning production reliability and customer experience.
Build and refine data pipelines and evaluation frameworks for autonomous-driving foundation models, curating rare driving events and measuring model progress to scale safe, generalizable AI for vehicles.
Own end-to-end supply chain for autonomous-vehicle programs, coordinating suppliers, logistics, and cross-functional teams to meet hardware build timelines and quality standards.
Develop and run automated tests for embedded software in autonomous-vehicle systems, ensuring reliability on Linux/QNX-based platforms and hardware-in-the-loop rigs.
Senior ML engineer optimizes PyTorch models for edge deployment, iterating from training to production-ready releases under tight latency and memory constraints for autonomous-vehicle systems.
Principal Engineer defining the embedded software architecture for Wayve’s autonomous-vehicle AI platform, integrating NVIDIA/Qualcomm compute with OS, middleware, and safety-critical code in C/C++.
Design and implement embedded security controls for autonomous vehicle software, including firmware and Linux systems, while collaborating with teams to ensure robust, hardware-agnostic security across fleets.
Build and maintain C++ software for autonomous-vehicle sensor systems, integrating cameras, LiDAR, and radar to stream data and run edge ML inference across a large fleet of cars.
Own and lead a key AI Driver work package for Wayve’s autonomous-vehicle software, coordinating engineers, researchers, and PMs to ship AI-powered driving features from strategy to delivery.
Own end-to-end supply chain for Wayve’s autonomous-vehicle programmes, coordinating suppliers, logistics, and cross-functional teams to meet hardware build and launch timelines.
Build and maintain security analytics pipelines and dashboards to automate evidence collection and measure control effectiveness for Wayve’s AI-driven autonomous vehicle platform.
Develops 3D foundation models and world models for autonomous driving systems, focusing on geometric vision, multi-view geometry, and sensor fusion (LiDAR, radar, camera) to enable vehicles to perceive and navigate complex environments.
Build and refine computer vision models to measure autonomous driving performance offline, adapting foundation models for scene understanding and benchmarking accuracy across conditions.
Lead a team building offline scene-understanding models for autonomous vehicles, turning on-vehicle models into robust validation systems that predict counterfactual outcomes and assess AV2.0 driver behavior.
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