Wayve is a UK self-driving technology startup that develops end-to-end neural network driving software for assisted and fully automated driving.
The role As the Technical Lead for Data Ingestion Systems within our Wayve Corpus division, you will play a pivotal role in Wayve's mission to revolutionise autonomous driving. Each day, our teams handle and process multiple petabytes of data collected from our fleet of autonomous vehicles and critical partner integrations. Your technical leadership will directly impact our ability to transform vast amounts of raw data from diverse internal and external sources into structured, actionable insights that power advanced machine learning models and groundbreaking research. By continuously innovating our ingestion and processing pipelines, you'll help accelerate the development of safer, more efficient autonomous driving technologies, enabling Wayve to maintain its position at the forefront of embodied AI. Key responsibilities Technical Vision & Roadmap - Define and execute a strategic technical roadmap for enhancing and scaling data pipeline capabilities. - Drive innovation in pipeline architecture to support dynamic and evolving use-cases. Pipeline Development & Innovation - Lead the design and implementation of advanced data pipeline features for efficient data ingestion, transformation, and distribution. - Normalise and unify multiple disparate data sources — including data ingested from external partners — into a consistent and optimised format tailored specifically for AI training and research needs. - Collaborate closely with robotics, ML engineering, and research teams to continuously enhance pipeline performance and functionality. - Develop robust interfaces and systems to reduce bottlenecks and improve reliability and scalability. Operational Efficiency & Excellence - Establish and maintain best practices for pipeline reliability, including comprehensive observability, alerting, and monitoring systems. - Manage initiatives aimed at minimising pipeline latency, failure recovery, and ensuring compliance with defined service-level agreements (SLAs). Collaboration & Cross-Functional Integration - Engage actively with cross-functional teams (robotics, ML, data governance) to ensure alignment of technical efforts with overall business goals. - Foster a culture of transparency, collaboration, and shared ownership across teams. Team Development - Mentor and develop engineering talent, promoting professional growth and technical excellence within your team. - Contribute to the hiring and onboarding processes to expand the capabilities of the data pipeline engineering team. About you In order to set you up for success at Wayve, we're looking for the following skills and experience. Essential skills - Strong experience (8+ years) in software engineering, specifically focused on developing and operating scalable, complex data pipelines, including at least 3+ years leading or architecting data engineering projects. - Proven technical leadership experience in a pipeline engineering or related domain. - Expertise in modern data pipeline architectures, including DAG-based orchestration (e.g., Airflow, Flyte, Ray). - Solid understanding of data engineering practices, distributed processing frameworks, and pipeline optimisation techniques. - Excellent communication and collaborative skills, capable of working effectively with interdisciplinary teams. - Track record of mentorship and talent development. - Bachelor's degree or higher in Computer Science, Engineering, or related technical discipline. Desirable skills - Experience with robotics or autonomous vehicle sensor data processing pipelines. - Familiarity with third-party dataset ingestion and transformation. - Understanding of compliance and data governance frameworks (e.g., GDPR, TISAX, ASPICE). - Hands-on experience integrating observability and monitoring solutions. This role is a full-time role based in Sunnyvale, CA (hybrid) and the reasonably estimated salary for this role ranges from $276,100 to $293,800, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience. #LI-HH1