You have built real AI systems that ship, break, and get fixed under pressure.You care less about model demos and more about decisions that move capital.You want your work used daily by professional investors, not buried in notebooks.You are comfortable owning hard problems end to end.If you want a calm job optimizing benchmarks, this is not it.The Role, In Plain EnglishYou will build and evolve the AI systems that power Reflexivity’s investment insights.This role exists because off-the-shelf models and generic pipelines are not enough.You will work at the intersection of reasoning engines, proprietary data, and real market impact.Your work directly affects how investors understand earnings, risk, and market catalysts.What You’ll Be Responsible ForDesign, fine-tune, and deploy ML and LLM-driven systems used in production by professional investorsBuild and maintain inference pipelines that are fast, observable, and reliableIntegrate OpenAI, Gemini, and Anthropic models into reasoning and knowledge systemsWork closely with backend engineers to productionize models in Golang-based servicesImprove signal quality, not just model accuracyReview code and designs with a bias toward long-term maintainabilityWhat “Good” Looks Like in This RoleAfter 3 months:You understand the product, data flows, and investor use cases deeply. You ship meaningful improvements.After 6 months:You own major parts of the AI stack. Your work improves insight quality and latency measurably.After 12 months:You are a technical reference point for AI decisions. You raise the bar for how AI is built at Reflexivity.Who You Are (Must-Haves)5 plus years building ML or applied AI systems in productionStartup experience working on a core product, not a side projectStrong Python skills and experience integrating with backend systemsHands-on experience with AI-assisted coding tools like Cursor or Claude CodeFintech experience or strong personal investment backgroundComfortable owning outcomes, not just tasksNice-to-Haves (Not Deal Breakers)Experience with LLM reasoning systems or knowledge graphsExposure to Golang-based ML integrationsPrior experience supporting investor-facing productsHow We WorkIn-office team with high trust and high ownershipDirect communication, minimal process, strong opinions backed by dataEngineers are expected to think about product impact, not just codeWe move fast when it matters and slow down when correctness matters moreWhy This Role Is Worth Your TimeDirect influence on how professional investors make decisionsHard problems at the edge of AI, data, and financeReal ownership and technical autonomySenior peers who care about quality and outcomesCompensation & PracticalitiesBase salary: £90,000 to £160,000 depending on experienceEquity includedIn-office role based in LondonNo agency candidates£90,000—£160,000 GBP #J-18808-Ljbffr