Staff Machine Learning Engineer - Pricing

📈 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 [link for members] came in at #6 on LinkedIn's Top Startups 2026 [link for members] in the UK, #7 on the Scaling Europe 50 [link for members] of Europe's fastest-growing companies, and #14 on Sifted's AI 100 [link for members]

Learn more about us on our website [link for members]

🏅 The Role

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

Rosso is the company'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.

• Strong probabilistic modelling and decision-making under uncertainty, such as pricing, conversion or elasticity modelling, and experimentation. You formulate the problem from first principles and pick the simplest approach that works.

• You've run ML as production services on modern cloud infrastructure: containerised models behind APIs, event-driven microservices, infrastructure as code and CI/CD on AWS or similar. Your model changes go through the same pipeline, tests and rollback as any other code, so shipping one on a Thursday afternoon is routine.

• Production-grade Python, and a habit of designing for observability, testing and replay. You work as a peer with software engineers.

• A track record of moving a commercial metric, working alongside product managers and commercial stakeholders to get there.

• You use AI tooling as a day-to-day multiplier on your own work, and you know where it fails.

Energy experience is not required. We will teach you the market. What we can't teach quickly is the judgement that comes from running a live pricing or trading system.

Bonus points

• Experience in energy, commodities or other markets where prices, forecasts and risk interact.

• Contextual bandits, causal inference or Bayesian methods used in production.

• Stochastic optimisation or operations research.

• A PhD in a field such as maths, physics, computer science or operations research.

✨ Benefits & Perks

• Salary: Competitive. We are looking to pay £115,000 or equivalent in local currency.

• Stock Options: everyone on the team has ownership in our mission.

• 25 days holiday + public holidays. Swap public holidays for ones that matter most to you. Plus, get an extra day off for your birthday 🎉.

• Remote working: we're fully remote, distributed across Europe

• Home working & wellbeing budgets:

• Up to £1,200 / €1,200 annually to upgrade your remote setup (co-working passes, equipment, etc.).

• Up to £150 / €150 monthly on anything that supports your wellbeing, from therapy to gym memberships to meditation apps.

🗣️ Interview Process

Our processes normally take around 2-3 weeks from first call to offer. Please let us know about any adjustments to timelines that may be required.

• First call with our Talent Team (30 mins). This is to understand your experience, motivations, and discuss the role in more detail.

• Behavioural interview with our Rosso GM, hiring manager for this role (30 mins). This is your chance to really understand the role, the expectations, and ensure alignment on ways of working.

• Technical interview with the Team (90 mins). You'll meet with potential peers in this session and work through a live technical exercise.

• Bar Raiser interview with Stakeholders (45 mins). The final session will be with two cross-functional stakeholders.

We welcome applications from people of all backgrounds, experiences, and identities, including those that are traditionally underrepresented in the tech and energy sectors. If you’re excited about this role but not sure you meet every requirement, we’d still love to hear from you. Your unique perspective could be exactly what we’re looking for.