Staff Machine Learning Engineer - Pricing

📈 Who We Are

We are 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.

🏅 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. This is a hands-on role where most of your week will be writing and shipping production code, staying accountable for what that code does once 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, including 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.

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.
• Redesign the team's recurring pricing workflows with AI built in, such as feedback triage, daily price review and experiment write-ups.

🎯 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.
• Production ML experience: You have owned a live ML system that made automated decisions with money attached (e.g., algorithmic trading, dynamic pricing, marketplaces, ad bidding, credit, or betting).
• Probabilistic modelling: Strong probabilistic modelling and decision-making under uncertainty (pricing, conversion, elasticity modelling, and experimentation).
• Modern cloud infrastructure: You've run ML as production services on modern cloud infrastructure (containerised models, event-driven microservices, IaC, CI/CD on AWS or similar).
• Production-grade Python: Habit of designing for observability, testing, and replay. You work as a peer with software engineers.
• Commercial impact: A track record of moving a commercial metric, working alongside product managers and commercial stakeholders.
• AI tooling: You use AI tooling as a day-to-day multiplier on your own work, and you know where it fails.

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 mathematics, physics, computer science, operations research, or a related field.

✨ Benefits & Perks

• Stock Options: Everyone on the team has ownership in our mission.
• Time Off: 25 days holiday + public holidays (with flexibility to swap) plus an extra day off for your birthday.
• Remote Working: Fully remote setup distributed across Europe.
• Home Working & Wellbeing Budgets:
  • Up to £1,200 / €1,200 annually to upgrade your remote setup.
  • Up to £150 / €150 monthly for wellbeing (therapy, gym memberships, meditation apps, etc.).