Jobs at Wayve

Lead and deliver complex, cross-functional engineering programs across AI, software, robotics, infrastructure, evaluation, data, and product. Define plans, manage risks and dependencies, improve efficiency and tooling, align stakeholders to business goals, and drive continuous improvement to make multi-team delivery predictable.

Lead end-to-end delivery of data and annotation operations: intake, estimation, scheduling, execution, QA, supplier coordination, and self-serve user enablement. Drive clarity, risk management, status communication, and continuous improvement to make data pipelines predictable and high quality across engineering, data science/ML, and external annotation partners.

Lead and grow a TPM function for the Data org, own delivery of data platform, pipelines, enrichment, labeling and dataset quality; run flagship programs, establish scalable governance, KPIs, and cross-functional alignment to improve data quality, throughput, cost, and time-to-insight.

The Staff ML Performance Engineer optimizes ML inference for edge devices, profiles performance bottlenecks, and implements improvements across systems while collaborating with model developers to enhance on-device performance.

Lead the Application Engineering team to develop autonomy features for US road conditions, managing engineers and collaborating with OEM partners.

Lead Wayve's new Application Engineering team to develop autonomous driving technology for the US market, focusing on robotics and machine learning systems.

The Learning Experience Partner will develop L&D strategies, design impactful learning experiences, and enhance organizational growth through feedback and data-informed programs.

As a Machine Learning Engineer, you'll develop ML-driven behaviors for autonomous driving, lead cross-functional projects, and mentor other engineers, ensuring performance and adaptability in production systems.

Design and evolve global compute and storage architecture for large-scale GPU training and inference across multi-cloud and data centers. Enable petabyte-scale data federation, cross-region GPU job execution, advise leadership on cloud strategy, and provide technical leadership and mentorship to advance AI model development and deployment.

The Product Manager will lead a cross-functional team to deliver the AI Driver product, focusing on strategy, communication, and technical detail in AI and ML.

As Director of AV Product Engineering, you will manage product engineering, develop autonomous vehicle capabilities, and lead multiple teams to ensure alignment with company objectives while fostering technical growth and innovation.

As a Machine Learning Engineer, you'll develop and enhance driving models, lead projects on personalized driving, and collaborate across teams to ensure system performance for automated driving.

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 Machine Learning Engineer will develop AI driver models, manage training algorithms, and conduct on-road evaluations to enhance automated driving safety and performance.

The Customer Integration Engineer supports clients during the development lifecycle by providing technical assistance, troubleshooting, and ensuring effective integration of AI technology into vehicles.

The System Architect - Robotaxi will define and oversee the system architecture for deploying Wayve AI Driver in robotaxi systems, ensuring integration across vehicles and platforms. Responsibilities include translating use cases into requirements and collaborating with internal and external partners.

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

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.

Responsibilities include defining technical architecture for vehicle integration, translating product needs into specifications, and collaborating with cross-functional teams.