At Sourceful, we’re not building another energy app — we’re creating the Home Energy Layer, the infrastructure linking solar panels, batteries, EVs, and meters. By turning households into sovereign energy nodes, we’re driving a bottom-up transition and building the missing link between millions of homes and a sustainable, resilient grid.
As a Senior Machine Learning Engineer, you’ll be central to this mission. Each connected home is a complex optimization challenge — multiply that by millions, add forecasts, grid signals, market prices, and user behavior, and the problem becomes enormous. You’ll solve this at scale with sub-second optimization, time series prediction, and self-learning systems coordinating energy across thousands of households in real time.
You’ll design and deploy:
Predictive Models – forecasting consumption, production, and pricing across households
Optimization Engines – millisecond-level decisions on charging, selling to grid, or powering the home
Self-Learning Systems – adapting to household patterns while improving global coordination
Edge Intelligence – lightweight ML on local devices linked with cloud intelligence
Market Dynamics – predicting demand, flexibility, and trading opportunities
We seek engineers with 5+ years in production ML, ideally in time series, optimization, or control. You’re fluent in Python (NumPy, PyTorch/TensorFlow, scikit-learn), own the full ML lifecycle, and understand scaling, monitoring, and drift. Energy knowledge is a plus; curiosity is essential. Bonus: experience in RL, multi-agent systems, or smart grid protocols.
This is an on-site role in Kalmar — breakthroughs happen when brilliant minds work side by side. We offer competitive pay, meaningful equity, and the chance to directly accelerate the renewable energy transition.