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The Staff Machine Learning Engineer builds and fine‑tunes computer‑vision and scene‑understanding models for offline performance measurement of autonomous vehicles, leveraging Python, PyTorch, transformer‑based vision models and large foundation models, and benchmarks their accuracy across platforms and conditions.
The Senior Site Reliability Engineer will ensure reliability, observability, and safety of Wayve's autonomous vehicle software fleet, handling on‑call support, building monitoring and automation, and driving incident response using Linux, Kubernetes, Docker, Python/C++/Rust, and tools like Prometheus and Datadog.
Train, debug, and ship computer vision and 3D perception models powering Wayve's ADAS driver-assistance products, working across the full ML lifecycle — building scalable data pipelines (incl. auto-labelling), training, evaluating, and iterating on detection, segmentation, and tracking. Involves both on-car latency-constrained models and offline large-scale data generation using CV/deep learning,
The Staff Security Engineer at Wayve will lead the security framework for the internal SPINE platform, developing and automating endpoint and identity security controls using scripting, IaC, and Linux, while collaborating with the central security team to enforce policies and mentor engineers.
Lead Technical Program Manager for Data at Wayve builds and leads a TPM team to deliver data platform, pipelines, enrichment, labeling, and dataset management for autonomous vehicle AI, partnering with engineering and focusing on data quality, throughput, cost, and time‑to‑insight using tools like Databricks, Kubernetes, and AI agents.
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.
The Technical Program Manager leads cross‑team delivery of simulation, evaluation and validation platforms for Wayve’s AI driver, coordinating roadmaps, risk, and release‑gating while driving metrics‑focused outcomes.
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++.
The Sensing Systems Engineer at Wayve owns the vehicle sensor stack, defining performance requirements, building validation frameworks, and coordinating integration across hardware, software, and ML teams to ensure reliable autonomous driving data.
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.
The Security Assurance Manager will design, implement and operate Wayve’s security assurance program, creating risk‑based methodologies, testing and automating control validation to support standards such as TISAX and ISO 21434, while collaborating with engineering teams in a cloud‑native, AI‑driven autonomous‑vehicle environment.
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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