Data / ML Engineer

Our founders have built energy flex markets, led transactions of billion-dollar businesses, and built software agencies that consistently deliver engineers in the top 3% globally. That pedigree shapes how we think, how we work, and what we expect from the people who join us.

The role

Our models don't live in notebooks. They forecast energy production and demand, score decisions inside agentic workflows, detect faults in fleets of field devices, and drive dispatch choices with money and grid stability attached. When the model is wrong, someone notices by lunchtime.

You'll own the full loop: the data pipelines that feed the models, the models themselves (classical ML earns its keep here: random forests and gradient boosting alongside time-series methods), and the deployment, monitoring and retraining that keep them honest in production. You'll work close to the operational systems your outputs drive, not three teams away from them.

What you will do

• Build and run feature and data pipelines for training and inference on high-volume, time-series-heavy data

• Develop, evaluate and tune models: forecasting, optimisation scoring, anomaly and fault detection

• Deploy models into production systems with proper versioning, monitoring and drift detection

• Work with our AI engineers to embed model-driven scoring inside agentic workflows

• Validate model behaviour against historical data and simulation before it touches operations

• Explain model decisions to clients and non-technical stakeholders in plain language

What you bring — Required

• Strong Python and SQL across the full ML lifecycle: data prep, training, evaluation, deployment

• Production ML experience: your models have run live, been monitored, drifted and been fixed

• Depth in classical ML (gradient boosting, random forests, regression) and knowing when it beats deep learning

• Serious time-series experience: forecasting, seasonality, late and messy data

• Engineering fundamentals: your pipelines are tested, versioned and reproducible

Strongly preferred

• Energy forecasting or optimisation experience (generation, demand, price)

• Streaming/event-driven data infrastructure (Kafka or similar)

• MLOps tooling: experiment tracking, model registries, automated retraining

• Flex market and energy systems experience is always a big bonus

Nice to have

• Optimisation methods (linear/mixed-integer programming) for dispatch-style problems

• LLM evaluation experience alongside classical ML

Why this role

Your models will move real assets and real money in near real time, and you'll see the feedback loop close daily. Small senior team, no research theatre, and the rare setup where the person who builds the model also owns how it behaves in the wild.

What you get

• A competitive package that reflects the calibre we hire at

• Access to every AI tool on the market (as long as it has passed security) with team support and budget to get you set up and moving fast

• Hard, interesting problems: real AI in production, real scale, real consequences. Not demos and decks

• Direct access to some of the most experienced operators in the country

• A team that will stretch you, back you, and make you better

The interview process

Our process is straightforward and moves fast: a short video interview (we know engineers hate this — the only people who don't hate it are in sales — but it's an important and necessary step, and if you can't push outside your comfort zone, you're not going to fit in here), then a technical deep-dive on real problems, not puzzles, then a final conversation with the founders. We move quickly for the right person.