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The role involves advising on commercial matters, drafting contracts, supporting strategic transactions, and building legal processes as Wayve scales globally.

Develop and ship end-to-end machine learning models for autonomous driving behaviors across L2-L4 systems. Own data curation, training, evaluation, experimentation, and on-road validation. Improve model performance through iterative training cycles while addressing real-world safety, quality, and deployment constraints. The role may involve reinforcement learning, transformer networks, automotive systems, model compression, and embedded deployment.

As a Staff Machine Learning Engineer, you will lead the development and deployment of AI models for autonomous driving, focusing on safety and reliability. Responsibilities include driving the roadmap, training models, and collaborating across teams.

Lead reinforcement learning development for end-to-end autonomous driving models. Responsibilities include designing offline and off-policy RL methods, improving reward models, building large-scale training and evaluation workflows, diagnosing distribution shift and optimization failures, validating policies through simulation and on-road testing, productionizing successful methods, and mentoring engineers. The role also leads development of learned emergency trajectory models for safety-critical maneuvers such as evasive steering and emergency braking.

Own defined portions of Robotaxi system architecture, translating use cases into requirements, interfaces, specifications, and verification plans. Collaborate with OEM, Tier1, and operator partners to establish technical boundaries and lead design discussions. Apply architectures across vehicle platforms, support integration and bring-up, triage issues, assess design changes, and address safety, redundancy, degraded operation, and offboard service considerations for production-intent L4 autonomous systems.

Develop and maintain Wayve’s embedded Linux software stack for autonomous vehicle fleets. Responsibilities include Linux kernel and device-driver development, YoCTO-based distribution maintenance, high-performance C/C++ software development, diagnostics, fault tolerance, debugging, and secure fleet hardening. The role collaborates across engineering, product, hardware, supply chain, and field teams to support reliable on-road experimentation and autonomous driving data collection.

Investigate autonomous vehicle issues across hardware, software, robotics, and AI models; analyze fleet and on-road data, identify root causes, reproduce problems through simulation and testing, and develop automated investigation workflows. Collaborate with engineering, operations, safety, and model development teams, respond to incidents, communicate findings, and improve AV performance and safety.

Leads complex, cross-functional technology programs across AI, software, robotics, vehicle hardware, infrastructure, simulation, data, and product. Owns project delivery, improves efficiency, manages risks and dependencies, aligns stakeholders, supports business goals, and drives continuous process improvement. The role requires strong technical understanding, systems thinking, communication, project management, and influence without authority across engineering teams.

Leads Wayve’s data strategy for autonomous-driving AI, including data acquisition, discovery, curation, enrichment, training, evaluation, deployment, and performance measurement. Builds the data flywheel, identifies rare and safety-critical scenarios, connects data investment to model outcomes, scales global data architecture, and leads a multidisciplinary Data organization. Partners with AI, Science, Validation, Product, Compute, Finance, and Commercial teams.

Build and operate production search and indexing systems at billion-vector scale for multimodal driving data. Develop embedding-based retrieval, metadata filtering, backend APIs, ingestion pipelines, and evaluation frameworks covering relevance, recall, latency, scalability, freshness, and cost. Own components from architecture through production operations, collaborate across ML and data teams, make build-versus-buy decisions, and mentor engineers.