Member of Technical Staff - Data EngineerComp confirmed on your call.
The role
Training frontier AI agents requires interactive digital environments where models can act, fail, and learn across real software. In roughly a year, this seed-stage research lab has scaled to over 50 engineers across London and Paris, partnered with multiple frontier AI labs, and secured backing from top-tier investors and founders. The core constraint on agent performance is data: acquiring, sanitising, and reliably serving massive, messy real-world datasets to the engineers building those training environments.
You will own the data platform powering these environments end to end, from architectural design to production debugging. This is not a BI or reporting seat, and it is not an isolated pipeline-plumbing gig. You will work across software, platform, and AI engineering boundaries to build high-throughput ingestion, automated PII sanitisation, and validation engines, staying close to production code rather than just configuring vendor dashboards.
The work
- Architect and operate scalable ingestion, processing, and storage systems that handle messy, high-volume datasets from real-world software environments.
- Build automated validation, sanitisation, and PII-redaction pipelines that make raw data immediately usable for AI training environments.
- Make pragmatic technical trade-offs across storage, retrieval latency, observability, system reliability, and cloud costs.
- Establish reusable data platform patterns that other software and AI engineers can build on, eliminating the need to solve dataset ingest on an ad-hoc basis.
- Debug unfamiliar production failures across the stack and deploy modern tooling to keep engineering feedback loops tight.
What you bring
- Proven track record designing, shipping, and operating substantial production data systems end to end, with personal ownership over the architectural decisions and outcomes.
- Strong software engineering fundamentals with the ability to dive deep into unfamiliar codebases and debug complex, distributed failure modes.
- Direct experience working through large, messy, or adversarial datasets where ingestion, validation, or sanitisation posed genuine technical bottlenecks.
- Demonstrated ability to take ambiguous requirements, define the necessary system architecture, and ship production systems without detailed functional specs.
- Based in or able to work hybrid from London or Paris.
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