Software Engineer, Data Ingestion

About the role

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.

Here’s what this particular role covers.

🛠️ About our Ingestion Systems Team

Our Ingestion Systems team sits within Wayve’s AI Platform organisation and builds the data foundations behind our self-driving technology. We process vast amounts of real-world driving data so it can be used for annotation, model training and evaluation. Operating at petabyte scale, the team focuses on making these critical data flows reliable, efficient and easy to operate.

🧠 Your day-to-day

- Debug and unblock failing ingestion pipelines, stalled jobs and processing queues

- Investigate corrupt, malformed or unexpected data from a variety of sources.

- Optimise Spark jobs and batch-processing pipelines for throughput, reliability and compute efficiency.

- Design and implement more resilient pipelines so individual bad data segments do not block wider workflows.

- Improve monitoring, retries and failure recovery across multi-step ingestion workflows.

- Help prioritise and unblock important datasets for downstream annotation, data science and model-training teams, and contribute to the technical direction of Wayve's ingestion platform.

🧩 What you’ll be working on

- High-volume pipelines that process real-world driving data for annotation, data science, model training and evaluation.

- Resilient systems that isolate problematic data rather than allowing a single bad segment to block a wider workflow.

- Orchestration across complex dependencies, queues, retries and priority data flows.

- Pragmatic improvements to the performance, cost and operability of our Spark-based data platform.

- Ingestion of varied data formats from partners, suppliers and other third-party sources.

🙌 You should apply if

- You have strong production experience with Apache Spark and Python.

- You have built, operated or debugged large-scale ingestion, ETL or distributed data-processing pipelines.

- You are comfortable diagnosing production failures involving corrupt data, stalled queues, retries and complex dependencies.

- You understand how to optimise high-throughput systems for reliability, performance and cost.

- You can learn an existing system quickly, work independently and deliver practical improvements in a fast-moving environment.

- Experience with Airflow, Flyte, Databricks, Delta Lake, Scala or Java would be an advantage.

🌱 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.