Founding ML Engineer - Tech Lead (CTO path) - Health tech start-up

Full-time · 12-month fixed term (funded) · £45,000 + equity · UK-based, London a bonus · Start 1 September 2026

About us

We're an early-stage consumer health-tech startup in stealth, building for the millions of people with chronic conditions who end up managing their health largely alone once standard care plateaus. We look at health as a system, from the consumer side, and we're building toward two outcomes: better day-to-day health outcomes in chronic disease and, long-term, feeding what we learn into how chronic health is understood, all the way up to policy. At the core is a hard ML research problem: saying something reliably useful for one individual from limited data, where calibration matters because the stakes are high. We're funded by a secured, non-dilutive UK innovation grant, won on an extensive evidence base with a waitlist, national partnership, a mapped regulatory position, including a 35-page bespoke regulator review, and a working, granular, custom automated go-to-market engine.

The founder is an engineer, scientist, and builder with a lifetime of research into their own disease, after a career in private equity and managing the allocation of $850M/yr.

The role

The next 12 months are a funded build, and this role is its technical core. You are hire #1, working directly with the founder, with a full-stack developer supporting you for the first four months. You own the technical side end to end: the models at the heart of the product and the platform they run on.

Over the year you will:

• Set the technical foundations.
• Set up the engineering practices you actually want to live with (code review, standups, issue tracking).
• Model the domain.
• Co-design a proprietary domain ontology with the founder and build the knowledge-graph layer on top of it.
• Build the extraction pipeline.
• LLM-driven information extraction from free-text and structured user inputs into the knowledge graph, with confidence scoring and human-in-the-loop validation.
• Build the inference layer.
• Work out which inference approaches - from hierarchical Bayesian models to causal ML and N-of-1 designs - give well-calibrated answers. The deliverables are benchmarks and calibration reports.
• Learn from a live cohort.
• Real users co-design and use the product from the first months. You'll analyse their data as it accumulates and recalibrate the models continuously; the strongest findings become our first research output.
• Work to a standard that survives scrutiny.

We operate deliberately inside a defined pre-regulatory boundary; your methods and evaluation documentation double as the evidence base for the regulated features that may follow.

The package

£45,000 gross, full-time PAYE, 12-month fixed term (1 September 2026 – 31 August 2027, tied to the funded project). The salary is fixed by our secured grant budget for the funded year, plus equity (details, vesting and scheme terms at offer stage). We plan to raise in parallel during this process. When the round closes, this seat is set up to convert to CTO, and we'll review compensation at that point. Start date 1 September 2026 - fixed by the funded project.

What you'll need

We’re looking for someone who’s interested in being a first hire and excited by our mission and the growth opportunities of a startup. We care about whether you can do this work, not how long you've been doing it. Early-career is welcome - finishing or recently finished PhD/MSc, or equivalent depth however you built it.

• Applied ML and statistics: probabilistic modelling (Bayesian methods), uncertainty, model evaluation and calibration.
• Working knowledge of modern NLP / LLM tooling - extraction, structured outputs, evals.
• Knowledge graphs: co-designing our ontology with the founder and building the graph it feeds. Built one before is ideal; strong data-modelling fundamentals and the drive to build one works too.
• Python + the ability to ship working software end to end and own the stack.
• Causal inference: individualised treatment effects, N-of-1 / small-data designs. If you have the statistical foundations and want to go deep here, we'll back you.
• Intellectual honesty about what the data does and doesn't show.
• Bonus: healthcare, patient-reported or observational health data, health-tech experience.

Nice to have

• Hands-on causal-inference work in practice.
• MLOps - continuous evaluation and monitoring (not initially needed).
• Healthcare or health-tech experience.

What we look for in you

Serious builder, low-ego, self-starter who takes initiative, intellectually honest, genuinely interested in improving our understanding of health and the outcomes of chronic conditions. Comfort building models that are calibrated and trustworthy, not just accurate - and staying honest about what the data doesn't show. A plus: experience in health-tech, consumer health or personal health (chronic condition, biohacking, longevity…) UK-based; London a bonus.

Process

Applications reviewed on a rolling basis - first began 2 August; apply early. The 1 September start means we can only consider candidates able to start then. If this excites you but you don't tick every box, apply anyway and tell us what you'd bring.