📈 Who We Are

We are rebuilding the energy transaction, making it transparent and fair.

We're rebuilding how energy is bought and sold. That means cutting out middlemen, connecting businesses directly with renewable generators, and ensuring every transaction is traceable and transparent.

We're a 160+ person team - operators, data scientists, engineers, customer champions and energy experts - backed by top-tier investors including Lightspeed Venture Partners, Hitachi Ventures, Allianz, AlbionVC and Atomico. We closed a $75M Series B round in early 2026, taking our total funding to $94M, and we now power energy for over 4,000 UK businesses across 8,500+ sites, with over £75m in value returned to businesses and generators so far.

We're a technology company solving the energy industry, not an energy company. Some people call us a neo-utility, the neo-bank idea, applied to energy.

The industry is noticing. We were crowned Europe's Most Exciting Tech Company at the GP Bullhound Allstars awards, came in at #6 on LinkedIn's Top Startups 2026 in the UK, #7 on the Scaling Europe 50 of Europe's fastest-growing companies, and #14 on Sifted's AI 100.

Learn more about us on our website.

🏅 The Role

We're hiring a Staff Machine Learning Engineer (L4) for Pricing in Rosso.

Rosso is tem's core IP. It forecasts prices and volumes, holds a live view of our portfolio, and sets the fees on every quote. You'll join the pricing ML team and own key components of our dynamic pricing system. The direction is set. Within your components, choosing the bets and proving whether they worked is yours. This is a hands-on role. Most of your week will be writing and shipping production code, and you'll stay accountable for what that code does once it's live.

You will:

Own key components of our dynamic pricing system end to end, from design through to running them in production.

Choose the bets within those components most likely to reduce sales adjustment, and show whether they worked.

Make your components observable, guarded and replayable, so a wrong price is caught before a customer sees it.

Unblock your own delivery across engineering, platform and commercial teams.

You will report into the ML Leader for Rosso.

🚀 Responsibilities

Own key components of dynamic pricing

Take ownership of one or more core components, such as the models that learn from won and lost deals, the experimentation and measurement layer, or the live guardrails that keep price changes safe.

Within your components, choose between competing bets based on which is most likely to move the metric, and stop the ones that don't.

Design experiments and holdouts that work with low-volume, high-value deals, where a textbook A/B test often isn't possible.

Run it live, safely

Build the monitoring, alerting, guardrails and staged release your components need. That includes detecting drift, catching runaway feedback loops and holding unexpected price moves for review.

Make any pricing decision your components produce reproducible after the fact, for debugging and for regulators.

Work with the Price Quoting squad and ML Platform so models are built for production from the start, not handed over at the end.

Close the loop with the business

Find out why sales teams adjust prices, and turn those reasons into model inputs or new bets.

Explain model behaviour and trade-offs to engineers, product and commercial colleagues clearly enough that they can act on it.

Redesign the team's recurring pricing workflows with AI built in, such as feedback triage, daily price review and experiment write-ups. Decide which steps stay human.🎯

🎯 Requirements

Must-haves

You build. Most of your current role is writing and shipping production code, and you want to keep it that way. This is not a people leadership role or an architecture role where others implement.

You have owned a live ML system that made automated decisions with money attached, for example in algorithmic trading, dynamic pricing, marketplaces, ad bidding, credit or betting. You have dealt with its failures in production, such as feedback loops, silent drift, bad outputs reaching customers and rollbacks, and changed how you build because of them.