Senior Staff Machine Learning Engineer – Pricing

Role Overview

• 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.

Tech Stack

• Python
• PyTorch

Benefits

• Offers Equity