🇫🇷 France · 23h ago
Staff Engineer
BAO
LinkedInleadEnglish-friendly
📍 London or Paris 💰 Competitive, plus meaningful equity 🏢 Hybrid, typically 2 to 3 days per week onsiteShape the data architecture behind the next generation of AIThe next generation of AI won't be built on static datasets alone. It will learn through active experience.They're a fast-growing AI infrastructure company building the foundations for this shift: interactive, resettable digital environments where AI agents can navigate software, complete tasks, learn from failure and improve through trial and error. They have engineering teams in London and Paris.Delivering those environments creates real engineering challenges around how data is acquired, processed, secured, evaluated and made available across the platform.They're looking for a Staff or Principal Data Engineer to help define how we solve those problems as we scale. This is a deeply hands-on individual-contributor role within Platform. You'll combine strong software and data engineering with architectural leadership, helping shape an important technical area while continuing to build.What you'll be doingOwn the technical direction for significant areas of our data infrastructureTake ambiguous data problems and turn them into clear architectures, systems and execution plansDesign scalable approaches to acquiring, ingesting, processing and integrating large datasetsBuild and evolve infrastructure for data quality, sanitisation, security and privacyMake architectural decisions around storage, processing, reliability, observability, scalability and costIdentify where existing approaches will stop working and lead the move towards better onesEstablish patterns and systems that other engineers can build on, rather than solving each dataset independentlyWork across Data, Platform, Software and AI Engineering boundaries to solve end-to-end problemsProvide technical direction to other engineers without needing formal line-management authorityRaise the engineering bar through design reviews, technical judgement and hands-on contributionStay close to code, production systems and debugging rather than becoming a purely architectural roleUse modern AI tools actively while applying strong engineering judgement to their outputAt this level, success isn't simply delivering a pipeline. You should be able to recognise the underlying technical problem, determine the right direction and create leverage for the wider engineering organisation.What we're looking forYou're an experienced technical leader who has spent significant time building data-intensive production systems, and you've developed strong opinions about architecture because you've seen different approaches work, fail and evolve in real environments.You'll likely have:Deep software and data-engineering fundamentalsA strong record of owning complex production systems end to endExperience defining architecture across multiple systems or significant technical areasEvidence of leading major technical work while remaining an individual contributorExperience working with large, complex or heterogeneous datasetsStrong judgement around distributed processing, storage, reliability and operational complexityExperience making consequential technical trade-offs rather than simply adopting established patternsThe ability to explain why an architecture was chosen, what failed and what you'd now do differentlyExperience influencing and coordinating engineers beyond your own individual implementation workStrong debugging and production instinctsHigh levels of initiative and comfort operating with incomplete informationThey're deliberately stack-agnostic. Your ability to reason about systems and architecture matters considerably more than whether your previous company happened to use our technologies.We'd be particularly excited if you haveDesigned or significantly evolved large-scale data platformsWorked in a highly data-mature engineering organisationExperience with distributed systems and large-scale processingWorked extensively with unstructured or semi-structured datasetsSeen multiple generations of data architecture and led meaningful migrations between themExperience with data acquisition, ingestion and dataset lifecycle at significant scaleBuilt systems for data quality, validation, lineage or provenanceExperience with privacy, anonymisation, PII detection or secure data processingBuilt infrastructure supporting ML, AI or research workloadsHelped establish engineering standards or technical direction across a team or organisationWorked in both mature engineering environments and faster-moving startup or scale-up settingsPrevious AI experience is useful but not required. We're more interested in your technical judgement, curiosity and your ability to apply what you've learned from complex data systems to a new generation of AI infrastructure.Sourced from LinkedIn. Relocantly aggregates public job postings; apply on the original site.