Founding Senior Applied AI Engineer - Causal and Experiments

🧠 Build and Research at the frontier of AI
This is a very exciting time for companies at the frontier of AI, and Unloq is scaling fast. We’re building the system of record for business decisions, held to one standard: every recommendation has to be defensible to a board. We’re VC-backed, going live with important enterprise customers, and building towards our Series A.
That makes this a rare chance to join a startup early in its growth, while the team is small, the big problems are still open, and the people who join now shape what Unloq becomes. As we grow, we’re building Unloq Research, our applied AI lab, and this role is one of its founding seats.
We’re looking for a one of a kind Founding Senior Applied AI Engineer to own causal inference and experiments, and prove what actually moves a company’s numbers before we tell a board to act on it. You’ll report directly to our CTO and work alongside our founding leads. Around 70% of your week is research, design, code and releases. The rest is leading your domain.
The Role
You’ll own the journey from a client’s decision to a proven cause.
Building the causal layer, live in Q1 2027: map what drives each client’s numbers, estimate effects from natural experiments, and refute every estimate before it’s used
Returning effects as honest ranges, by how much and with what lag, so nothing prescriptive ships until it’s calibrated
Designing experiments with clients, such as a price change on one segment, and turning forecasts and causal estimates into “if we do X, here’s the range”
Owning the attribution method that credits each decision with the result that followed
Designing the evaluation that decides what ships: benchmarks, scoring rubrics and blind holdouts, and the statistics that make them trustworthy
In your first 90 days, you’ll learn the platform on real client questions, ship a first causal estimate with honest ranges on a real client problem, and move our evaluation from a single quality score to rubric-based checks with a blind holdout.
Who This Is For
A degree from a top-tier institution, with AI-native training and learning
Around five years or more building AI or ML systems that run in production
Causal inference in practice. Non-negotiable, and how we’ll assess you. You’ve estimated the effect of a real intervention on business data (difference-in-differences, synthetic control, uplift, marketing-mix models or experiments) and you know when each breaks on short histories
Strong statistics: uncertainty, calibration and experiment design
Evaluation design. You’ve built benchmarks that decided what shipped, and you know how to check that the evaluation itself can be trusted
Very strong Python (async, typed, tested) and solid SQL. You’ve deployed and maintained systems people rely on, on AWS or Azure
Real LLM depth: at least one RAG, tool-using or text-to-SQL system taken properly beyond a demo. You know LangChain, LangGraph and LlamaIndex, and when you’re better off writing it yourself
AI-native. Claude Code or similar every day, and you own every line you merge
You write your design down before you write code, and you can explain cause and effect to a CFO in plain English
Nice to have: time series forecasting, Bayesian methods, fine-tuning, ML served and monitored in production, Databricks, a paper or open-source work, having led a small team.
You’ll do well here if you can already answer these:
A client ran a promotion in peak season, alongside a price cut. With 24 months of data, how would you estimate what the promotion alone was worth?
How do you know an AI judge is right before you trust its scores?
Role Basics
Role: Full-time Founding Senior Applied AI Engineer, Causal and Experiments
Location: Remote, UK
Compensation: Competitive salary + founding-team equity
Start: ASAP
Process: a conversation with our CTO, a technical round with our founding leads, a case study, and a final round with leadership
Why Join Unloq?
Found the lab. You’ll lead the causal pillar of Unloq Research from day one, and shape how its methods reach the platform.
Real science, real ownership. Causal is the one domain nobody owns yet, and the prescriptive engine is built on your methods.
Direct founder access. You report to the CTO. Small team, big problems, zero bureaucracy.
Modern stack. Anthropic Claude, AWS, Azure and Databricks at the core.
We’re building the system of record for business decisions, so AI is trustworthy enough for the boardroom and not only the back office. It’s early, it’s hard, and the right people find that exciting. If you’d rather prove a cause than guess at a correlation, this is your role.

Work arrangement
Yes