Principal MLE - Revenue Optimisation

Requirements

• Deep experience building ML systems for pricing, revenue optimisation, or real-time decision-making - at companies where pricing is the product, not a supporting function. Track record of models that reached production and moved commercial metrics.
• Strong foundation in stochastic optimisation and probabilistic modelling. The judgement to formulate ambiguous business problems mathematically before reaching for a tool.
• First-principles reasoning across methods. You choose between stochastic programming, reinforcement learning, classical ML, or a simple heuristic based on what the problem demands.
• The engineering depth to match your modelling. Production-grade Python, high bar for code quality, and the

Additional Information

📈 Who We Are: We are rebuilding the energy transaction, making it transparent and fair. Our goal is to put power back where it belongs, in the hands of customers and to take on one of the most critical problems of our century, access to low cost electricity. tem exists to fix a broken global energy market that's long favoured legacy operators, intermediaries, and opaque pricing. Today's electricity system was not designed for rapid decarbonisation, AI-driven efficiency or fair access for the actual users - businesses and generators. We've built the first AI native transaction infrastructure to reinvent how electricity is bought, sold and priced. Our technology is designed to cut out the inefficient fees, automate complex market flows, and bring transparency and fairness to energy transactions at scale. In late 2025, after extraordinary growth, we closed a $75 million Series B - led by Lightspeed Venture Partners with participation from Albion, Atomico, Allianz, Hitachi Ventures, Hitachi Ventures, Schroders Capital and others - positioning us for global expansion, deeper product innovation and category leadership. We're scaling internationally and building toward a future where AI-driven infrastructure is foundational to electricity markets worldwide. Since launch, our modern utility product, known as RED, has already facilitated thousands of business customers and billions in energy transaction value, proving that modern software and AI can transform an industry built on legacy systems. At tem, we're not just building another energy company, we're rearchitecting market infrastructure so that transparency, efficiency and sustainability become the default, not the exception. 🏅 The Role Rosso is tem's core IP. It's the transaction infrastructure that replaces what a traditional trading desk does - forecasting energy prices and volume, building a real-time picture of the portfolio, optimising the fees placed on every quote, and autonomously managing hedging decisions. All of it running continuously. All of it on the critical path for every deal tem closes. Machine learning is at the heart of it. Rosso combines 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. We've proved the concept. tem now serves 2% of the UK market. The next step is building a pricing engine that doesn't just react - one that proactively drives growth by targeting the right customers at the right time, at the right price, while protecting margin and portfolio balance. Then taking that internationally. We're looking for a Senior Staff Machine Learning Engineer to own pricing ML within Rosso. This is a hands-on senior IC role with real technical authority - you set the strategy, define the mathematical approach, build the models, and ship them. You work closely with MLOps and software engineers, but you don't wait on them. The hard part of this job is the formulation, not the infrastructure. 🚀 Responsibilities Own the technical direction for pricing ML. Define what to build and how. Set the roadmap for the pricing engine as a core piece of tem's IP - and be accountable for its performance. Formulate and solve the pricing problem properly. The mathematical foundation doesn't fully exist yet. Your first job is to define it: a dynamic, real-time system that simultaneously optimises for signing probability, portfolio balance, and margin. Choose the right approach - stochastic programming, reinforcement learning, classical ML, or a hybrid - based on the problem, not familiarity. Build and ship models end-to-end. Own the modelling and data layer. Write production-grade Python. Architect models with deployment in mind and carry them through to production - you can execute without being blocked by engineering dependencies. Solve imbalance problems. Develop probabilistic models to optimise risk management and short-term balancing decisions in a highly dynamic environment. Be the voice of pricing ML across the business. Commercial, product, and engineering teams depend on this engine. They need to understand what it's doing and why. You make that happen - clearly, without losing precision. 🎯 Requirements

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