<h3>Why this role exists</h3><p>You will turn research ideas into reliable production systems operating on high-frequency time-series data in an energy market context.</p><h3>What you’ll do</h3><ul><li><p>Develop and productionize ML models for forecasting and optimization</p></li><li><p>Design and run experiments, training pipelines, and backtests</p></li><li><p>Collaborate with Data Engineering on feature pipelines</p></li><li><p>Ensure models are reproducible, testable, and monitorable</p></li><li><p>Write clean, production-quality code</p></li></ul><h3>What we’re looking for</h3><ul><li><p>Master’s or PhD in a quantitative field</p></li><li><p>3+ years experience as ML Engineer, OR Engineer, or Researcher</p></li><li><p>Strong ability to write clean production code</p></li><li><p>Solid Git and Docker workflow experience</p></li><li><p>Hands-on experience running ML experiments, training pipelines, and evaluations</p></li></ul><h3>Nice to have</h3><ul><li><p>Research background</p></li><li><p>Experience with Deep Learning, Linear Programming, Stochastic Optimization, Reinforcement Learning, or time-series modeling</p></li><li><p>Experience with meteorological or weather data</p></li></ul><h3>What you get</h3><ul><li><p>End-to-end ownership from experiment to deployment</p></li><li><p>Real-world impact in a high-leverage domain</p></li><li><p>Competitive compensation and meaningful equity</p></li><li><p>Close collaboration with a technical founding team</p></li></ul><h3>How we work</h3><p>We prioritize rigor, speed of learning, and honest evaluation. Clear communication, high autonomy, and strong execution standards.</p>

<p>Marbl delivers automated trading optimization for flexible energy assets. Our system generates dispatch schedules and market actions tailored to each asset’s technical constraints, operational preferences, and risk profile. Built for MW-scale batteries, hybrid renewable portfolios, virtual power plants, and industrial flexibility, Marbl combines quantitative optimisation, algorithmic trading logic, and high-speed market integration to turn flexibility into measurable revenue without adding operational complexity. The platform is modular and API-driven, designed to integrate with asset control systems and market access providers. The result is stronger trading performance, faster reaction to market signals, and scalable execution without adding overhead costs as portfolios grow.</p>

Machine Learning Engineer (Forecasting / Optimisation)

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marbl

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