The problemEnergy Market is the big brother to the stock market and it has similar trading mechanisms and similar forecasting problems and we are building the first AI energy trader that makes energy trading more accessible and profitableEnergy prices change every few minutes. Batteries can profit from that volatility, buying low, selling high, and getting paid to stabilize the grid, but only if the trading decisions are faster and smarter than any human can manage.That’s what we’re building at Solship: an AI that trades energy autonomously, 24/7. Our models run millions of simulations per minute to decide whether a battery should charge, discharge, or hold. Every decision has direct profit and loss impact, and we’re already generating 2× revenue for pilot partners. What you’ll own• Forecasting models — design and train the models that predict energy prices, market signals across multiple markets• Real-time dispatch optimization — build the optimizer that decides battery actions every few minutes, combining ML and stochastic methods under hard physical constraints• Production ML pipeline — deploy, monitor, and improve models that execute live trades autonomously. Uptime and accuracy matter here as much as research quality.• Performance infrastructure — build the evaluation layer that tracks model performance against live market outcomes and flags degradation before it costs money• Product surface — contribute to the frontend (React/TypeScript) that makes forecasts, trades, and performance transparent to customersWhat you’ll get• You will be building An AI Trader nothing more cool than that — It is one of the most interesting applied ML problems out there• Equity — you’re a founding engineer, not employee #47. You’ll have real ownership in what we’re building.• Full technical ownership — you’ll define the ML architecture, choose the tools, and set the direction. No layers of approval.• Flexible, async work — we care about output, not hours logged. Work from wherever you do your best thinking. (Honest note: You will work a lot) What we’re looking forMust-haves• 3-7 years Hands-on experience with ML-based optimization — MPC, reinforcement learning, MLIP, stochastic optimization, or similar constrained decision-making problems• Background in time-series forecasting and control systems• Very strong Mathematical and Modelling skills• Strong Python engineering skills and a track record of shipping ML models to production• Comfort operating with high autonomy in a small team where you’ll define the technical direction, not just follow itStrong advantages• Experience with energy markets, electricity pricing, or grid operations• Full-stack ability (React / TypeScript) for contributing directly to the product