Machine Learning Engineer

You build and deploy models used directly in electricity trading. You will take ideas from research papers through implementation large scale experimentation and deployment into production systems that operate under real market constraints. You will work with large high frequency time series data and ensure models are reproducible testable and monitorable.

Tasks

• Develop and productionise ML models used for forecasting optimisation and decision support
• Design and run ML experiments training pipelines and backtests
• Collaborate with Data Engineers on data pipelines and feature generation
• Ensure models are reproducible testable and monitorable in production
• Write clean maintainable well tested production code

Requirements

Core skills

• Masters or PhD in a quantitative field (ML statistics physics applied maths engineering)
• 3 or more years of experience as an ML Engineer or Engineer/Researcher in a quantitative role
• Proven ability to write clean production quality code
• Strong experience with Git and Docker in ML workflows
• Hands on experience running ML experiments training and backtests end to end
• Comfortable with mathematical optimisation and training ML or DL models
• Professional English (C1) is required

Nice to have

• Academic or industrial research experience
• Experience with deep learning linear programming stochastic optimisation reinforcement learning or time series analysis
• Experience working with meteorological or weather data
• Familiarity with trading or financial data electricity markets or power systems

Benefits

• You move in flat hierarchies and have short decision making paths
• End to end ownership from modelling through deployment
• Close collaboration with a small senior team and fast iteration cycles
• Flexible hybrid setup depending on location
• Compensation is based on the IT collective agreement (38.5 hours/week) with willingness to overpay depending on experience and qualifications

Electricity markets are entering a new era. As renewables storage and flexible demand scale price dynamics get sharper faster and more volatile. Winning is no longer about reacting well it is about making high quality decisions continuously. marbl is building the algorithmic flexibility trading layer for this market reality. We turn market data into forecasts and optimisation driven actions that can run live so trading teams can scale short term decision making across portfolios without scaling headcount or operational risk. The outcome we are driving is simple: make flexible assets easy to trade well. Better decisions better execution stronger economics and faster progress toward a low carbon power system.