Principal MLE - Revenue Optimisation

tem

Job Overview

Location

United Kingdom

Job Type

Full-time

Full Job Description

📋 Description

• Principal MLE - Revenue Optimisation role at tem, focused on owning pricing ML within Rosso, the company's core transaction infrastructure that replaces traditional trading desks by forecasting energy prices, optimising fees, and managing hedging decisions autonomously.
• Day-to-day responsibilities include defining the technical direction for pricing ML, formulating and solving the pricing problem using stochastic programming, reinforcement learning, or classical ML, building and shipping production-grade Python models end-to-end, solving imbalance problems via probabilistic risk management, and serving as the voice of pricing ML across commercial, product, and engineering teams.
• tem is rebuilding the global energy transaction system to make it transparent and fair, having built the first AI-native transaction infrastructure that cuts inefficient fees, automates market flows, and brings transparency to energy transactions at scale, with its RED product already facilitating thousands of business customers and billions in transaction value.
• The role offers the opportunity to shape a pricing engine that proactively drives growth by targeting the right customers at the right time and price while protecting margin and portfolio balance, with international expansion on the horizon following a $75M Series B funding round led by Lightspeed Venture Partners.

🎯 Requirements

• Deep experience building ML systems for pricing, revenue optimisation, or real-time decision-making at companies where pricing is the product, with a track record of models reaching production and moving commercial metrics
• Strong foundation in stochastic optimisation and probabilistic modelling, with the judgement to formulate ambiguous business problems mathematically before selecting tools
• First-principles reasoning across methods — choosing between stochastic programming, reinforcement learning, classical ML, or heuristics based on problem demands rather than familiarity
• Engineering depth to match modelling ability: production-grade Python, high code quality standards, and capability to carry models from formulation to deployment without engineering blockers
• Senior technical leadership experience: setting direction for significant technical areas, influencing cross-functional teams, and translating complex model behaviour into clear terms for stakeholders

🏖️ Benefits

• Opportunity to own and shape tem's core IP (Rosso) as a hands-on senior IC with real technical authority and strategic influence
• Work on rebuilding the global energy market to make it transparent, fair, and efficient — tackling one of the century's most critical problems
• Join a well-funded scaleup post-$75M Series B with backing from Lightspeed, Atomico, Allianz, Hitachi Ventures, and others, positioned for global expansion
• Impact visible through tem's RED product, which already facilitates thousands of business customers and billions in energy transaction value
• Collaborate with a mission-driven team focused on decarbonisation, AI-driven efficiency, and fair access to low-cost electricity

Skills & Technologies

Python

React

PyTorch

Senior

Hybrid

Degree Required

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About tem

tem is an energy technology company revolutionizing how businesses procure electricity and generators sell power. Through its innovative RED™ platform, tem helps businesses significantly reduce energy costs by eliminating waste from the traditional system, often achieving savings of up to 30%. The platform offers transparent billing and connects users with UK-based renewable generators, fostering a fairer value chain. Businesses gain total clarity over their energy usage and costs, while generators benefit from increased earnings for their output. With a mission to provide universal access to low-cost electricity, tem has already empowered over 3000 sites to optimize their energy transactions, driving efficiency and sustainability in the energy market.

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