[Hiring] Senior Staff Machine Learning Engineer - Pricing @tem

Role Description

Rosso is tem's core IP, the transaction infrastructure that prices electricity for thousands of businesses, balances portfolios in real time, and sits on the critical path for every deal tem closes. Machine learning is at the heart of Rosso, combining forecasting, optimisation and classical ML to process billions of data points and drive thousands of automated decisions a day. Every inference shapes the prices our customers see, so you can immediately see the impact of your work.

We have proved the concept with MVPs and POCs to grow to 2% of the UK market. Now we want to take it to the next level and build towards a state of the art solution, to fuel our expansion in the UK and take Rosso international.

We're looking for a Senior Staff Machine Learning Engineer to lead pricing ML within Rosso, building a platform that proactively drives growth by targeting the right customers to sign at the right time. Your primary focus will be the pricing engine, which sets the fees added to every quote tem serves, carefully balancing growth and margin. You will also contribute to the systems which manage both short and long-term imbalance decisions to determine how tem deals with its exposure across its portfolio.

This is a hands-on, senior individual contributor role with significant technical leadership and organisation-wide influence. You will work closely with other MLEs, software engineers and MLOps to bring models to production, and carry real ownership of the technical direction and accountable for its performance.

The right person is energised by the greenfield environment: comfortable taking on ambiguity and able to make progress before the path is fully defined. They have built pricing systems that worked and have learned from the times it hasn't. They'll bring that hard-won judgment to a system where the foundations are still being laid, and where early decisions compound. Success will be turning our current reactive system into a pricing engine which proactively drives growth by targeting the right customers to sign at the right time.

Responsibilities

• Own the technical direction for pricing ML:
• Define what to build and how within the pricing engine, setting the strategy and roadmap for pricing machine learning as a core piece of tem's IP.
• Build ML systems for price optimisation:
• Design and implement models that dynamically set prices, balancing the trade-off between signing probability, portfolio balance and margin maximisation.
• Solve imbalance problems:
• Develop probabilistic models to optimise risk management and short-term balancing decisions in a highly dynamic environment.
• Bridge modelling and production:
• Own the modelling and data layer while working closely with software engineers and MLOps to ensure models are architected for production, contributing to system design decisions that affect performance and reliability.
• Communicate pricing decisions clearly:
• Articulate model behaviour, assumptions, and trade-offs to other technical stakeholders so that pricing decisions are understood across the teams that depend on them.

Requirements

• Deep experience building ML systems for pricing, revenue optimisation, or decision-making under uncertainty, with a track record of models that went from concept to production and delivered measurable commercial impact.
• Strong foundation in stochastic optimisation and probabilistic modelling, with the judgement to formulate ambiguous business problems as the right mathematical approach rather than reaching for familiar tools.
• Proven first-principles reasoning: you choose between stochastic programming, classical ML, reinforcement learning, or a simple heuristic based on the problem, not the technique you know best.
• The engineering craft to match your modelling depth: production-grade Python, a high bar for code quality and system design, and the ability to work alongside software engineers as a technical peer across the full ML lifecycle.
• Senior technical leadership in ML: a track record of setting direction for a significant technical area, influencing cross-functional teams, and translating complex model decisions into clear terms for commercial, product, and engineering stakeholders so they are understood and acted on.

Bonus Points

• Experience with reinforcement learning or causal inference in applied, commercial settings.
• Familiarity with energy markets, power trading, or portfolio management.
• PhD or equivalent research depth in a quantitative discipline (statistics, applied mathematics, physics, operations research, or similar).
• Ability to reason about the trade-offs between optimisation solvers (Gurobi etc) and gradient-based ML methods (PyTorch etc), and the judgement to know when to reach for each.
• Experience working with high data throughput systems in production.

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.
• Behaviour Interview with our Head of Data (60 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.
• Culture-Add Interview with Stakeholders (45 mins): The final session will be with two cross-functional stakeholders, and will explore how your values align with ours, and is designed to be a genuine two-way conversation, your chance to understand what it's really like to work at tem.