Member of Technical Staff - Data Engineer

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

Job summary

Dex is building a seed-stage research lab in London and Paris, focused on training frontier AI agents. This role owns the data platform powering these environments end to end, from architectural design to production debugging, working across software, platform, and AI engineering boundaries.

You will build high-throughput ingestion, automated PII sanitisation, and validation engines, staying close to production code.

Qualifications

• Proven track record designing, shipping, and operating substantial production data systems end to end.
• Strong software engineering fundamentals; able to debug complex, distributed failures.
• Experience with large, messy datasets and data ingestion/validation/sanitisation challenges.
• Ability to take ambiguous requirements and ship production systems without detailed specs.
• Based in or able to work hybrid from London or Paris.

Responsibilities

• Architect and operate scalable ingestion, processing, and storage systems for high-volume datasets.
• Build automated validation, sanitisation, and PII-redaction pipelines for AI training data.
• Make pragmatic trade-offs across storage, latency, observability, reliability, and cost.
• Establish reusable data platform patterns for other engineers to leverage.
• Debug production failures and deploy modern tooling to keep feedback loops tight.

Skills

Data platform design

Production data systems

Distributed systems debugging

Data ingestion pipelines

Data validation/sanitisation

Cloud architecture

Hybrid work (London/Paris)

Job description

This role is with one of Dex's trusted partner companies. We work closely with their teams to truly understand their culture, goals, and what they're looking for, so we can match you with the right opportunity and give you context about the role before you commit to a process.

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 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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