Staff Software Engineer, Ingestion Systems

Wayve; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Wayve is seeking a Technical Lead for the Data Ingestion Systems within the Wayve Corpus division. You will set the technical direction for ingestion and processing pipelines and lead their design and implementation, unifying external and internal data for AI training and research.

You will collaborate with robotics, ML engineering, research and data governance teams, mentor engineers, and help hire and onboard new talent to build reliable, scalable data infrastructure.

Qualifications

• 8+ years in software engineering focused on scalable data pipelines.
• 3+ years leading or architecting data engineering projects.
• Experience with modern data pipeline architectures and DAG-based orchestration.

Responsibilities

• Define and execute a technical roadmap for data ingestion and processing pipelines.
• Unify multiple data sources into a consistent format for AI training and research.
• Lead design and implementation of pipeline features for ingestion, transformation and distribution.
• Mentor engineers and collaborate with robotics, ML, research and data governance teams.

Skills

Data pipelines

Technical leadership

DAG orchestration

Communication

Mentoring

Education

Bachelor's degree in CS/Engineering

Tools

Airflow

Flyte

Ray

Job description

Before the detail, here's the challenge you'd help us solve.

We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.

About our Engineering Teams

The Data Ingestion Systems team sits within our Wayve Corpus division. Every day, our teams handle and process multiple petabytes of data from our autonomous vehicle fleet and from partner integrations. We turn raw data from many internal and external sources into structured data that powers our machine learning models and research.

Your day-to-day

As Technical Lead, you'll set the technical direction for our ingestion and processing pipelines and lead their design and implementation. You'll unify disparate data sources, including data from external partners, into a consistent format built for AI training and research. You'll work closely with robotics, ML engineering, research and data governance teams, mentor engineers in your team, and help hire and onboard new ones.

What you'll be working on

• Defining and executing a technical roadmap for enhancing and scaling data pipeline capabilities
• Evolving pipeline architecture to support new and changing use cases
• Leading the design and implementation of pipeline features for efficient data ingestion, transformation and distribution
• Normalising and unifying multiple data sources, including external partner data, into a consistent, optimised format for AI training and research
• Building interfaces and systems that reduce bottlenecks and improve reliability and scalability
• Setting best practices for pipeline reliability, including observability, alerting and monitoring
• Leading work to reduce pipeline latency, improve failure recovery and meet SLAs
• Building a culture of transparency, collaboration and shared ownership across teams

You should apply if

• You have 8+ years in software engineering focused on developing and operating scalable, complex data pipelines, including 3+ years leading or architecting data engineering projects
• You have technical leadership experience in pipeline engineering or a related domain
• You have expertise in modern data pipeline architectures, including DAG-based orchestration (e.g. Airflow, Flyte, Ray)
• You have a solid understanding of data engineering practices, distributed processing frameworks and pipeline optimisation techniques
• You communicate and collaborate well with interdisciplinary teams
• You have a track record of mentoring and developing engineers
• You have a Bachelor's degree or higher in Computer Science, Engineering or a related technical discipline

Nice to have

• 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

Not ticking every box? That's totally okay! If you're passionate about autonomy and keen to learn, we encourage you to apply even if you don't meet every requirement.

More about Wayve:

Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines.

Our ambition is to make autonomy universal. Wayve's mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility.

Locations & Flexible Working

Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time w