Senior Staff Machine Learning Engineer - Pricing
New
T
temEnergy markets
Location: United Kingdom; Workplace: Remote, clear core hoursFull-TimeStaff
SalaryBase Salary £127K • Offers Equity
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Job Details
- Required Skills
- PyTorch
Requirements
- Have deep experience building ML systems for pricing, revenue optimisation, or decision-making under uncertainty.
- Show a track record of taking models from concept to production and delivering measurable commercial impact.
- Have a strong foundation in stochastic optimisation and probabilistic modelling.
- Be able to formulate ambiguous business problems using an appropriate mathematical approach.
- Use first-principles reasoning to choose between stochastic programming, classical ML, reinforcement learning, or heuristics.
- Have production-grade Python skills and a high bar for code quality and system design.
- Be able to work alongside software engineers as a technical peer across the full ML lifecycle.
- Have a track record of setting direction for a significant ML technical area and influencing cross-functional teams.
- Translate complex model decisions for commercial, product, and engineering stakeholders.
- Bonus: experience with reinforcement learning or causal inference in commercial settings.
- Bonus: familiarity with energy markets, power trading, or portfolio management.
- Bonus: PhD or equivalent research depth in a quantitative discipline.
- Bonus: ability to reason about optimisation solvers such as Gurobi and gradient-based ML methods such as PyTorch.
- Bonus: experience with high-throughput production systems.
Responsibilities
- Set the technical direction, strategy, and roadmap for pricing machine learning in the pricing engine.
- Design and implement models that dynamically set prices while balancing signing probability, portfolio balance, and margin.
- Develop probabilistic models for risk management and short-term balancing decisions.
- Own the modelling and data layer and work with software engineers and MLOps to bring models into production.
- Contribute to system design decisions affecting model performance and reliability.
- Explain model behaviour, assumptions, and trade-offs to technical stakeholders.
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