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# Senior Staff Machine Learning Engineer - Pricing

- **Company:** TEM, LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $312,000
- **Contract:** Permanent contract
- **Skills:** Software Quality, Python (Programming Language), Machine Learning, Software Systems, Reinforcement Learning, Pytorch, Data Layers, Production Code, Machine Learning Operations
- **Published:** October 29, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pvblmtfsj6

## About the Role

* 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.
 * A quantitative background in a domain where prices, forecasts and risk interact: energy markets, power trading, batteries and flexibility, market making, or similar. Energy is strongly preferred and where our strongest fits have come from.
 * 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.
 * Breadth across the full lifecycle: you carry the modelling and quant work and the productionising (deploying, monitoring, reasoning about the software systems around your models), and you would rather ship the whole thing to a good standard and iterate than perfect one component. You reason at the level of the outcome, the portfolio and its levers, not a single model.

Bonus points:

 * Experience with reinforcement learning or causal inference in applied, commercial settings.
 * 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.

## Description

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. You own the technical direction of pricing by building it: most of your week is writing and shipping production code. It is not a people leadership role, and not an architecture role where others do the implementing.

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, trading or optimisation systems that worked, ideally in energy or another market-driven domain, and have learned from the times they haven'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.