ML Engineer

About Us

Local Energy is building software for the next generation of industrial energy systems. We help industrial sites and energy infrastructure operators recover waste heat, optimize energy flows, reduce peak demand, and operate complex energy systems more intelligently. Decarbonization isn’t just our goal, it’s our core mission. Through AI-powered simulation and optimization tools, we enable the design of smart local grids and unlock dynamic energy markets. Our platform helps reduce emissions, drive efficiency, and create shared economic value, all while building more sustainable, resilient energy systems.

We are supported in part by Mila, a world-leading AI research institute, to build innovative solutions for the climate crisis focused on industrial local energy networks. We’re early-stage, funded, and growing fast and now we’re building out our engineering team from the ground up.

The Role

We are looking for an ML Engineer who can build applied forecasting and predictive modelling systems for industrial energy applications.

You will work on real time-series problems involving energy demand, load profiles, renewable production, operational signals, and other variables that influence how energy systems are designed and operated.

The role is intentionally practical and high-ownership. You should enjoy working with messy data, testing different modelling approaches, making trade-offs, and delivering models that are useful inside real product and client contexts.

You should be excited by questions like: • How do we forecast the evolution of complex energy demand over time? • How do we make predictive outputs usable inside planning, simulation and operational workflows? • How do we connect forecasting, optimization, and future control systems in a practical way? • How do we build models that are robust enough for physical infrastructure, not just benchmark datasets?

What You’ll Do • Build predictive components that estimate the evolution of key system variables over time, including energy demand, load profiles, renewable production, and other operational drivers. • Develop end-to-end modeling pipelines, from raw data preparation and feature engineering to model evaluation and practical delivery. • Compare and apply methods ranging from statistical forecasting approaches to modern sequence models and structured temporal models. • Work with time-series data from industrial and energy systems, including imperfect, incomplete, and heterogeneous datasets. • Integrate forecasting outputs into planning, simulation, and operational workflows so they improve decision-making rather than remaining standalone analysis. • Collaborate with the software and product teams to make sure models are usable, maintainable, and aligned with the needs of the platform. • Contribute to the foundation for future optimization and control capabilities, including adaptive MPC, system identification, and control-oriented learning systems. • Help shape the technical direction of Local Energy’s predictive intelligence layer.

What We’re Looking For • Strong experience in applied machine learning, time-series forecasting, or quantitative modelling. • Strong coding skills and experience building real modelling pipelines. • Experience with at least one modern ML framework, ideally PyTorch or JAX. • Good practical judgment in real-world modelling settings, including data quality issues, model evaluation, and deployment constraints. • Ability to work independently while collaborating closely with a small technical team. • Interest in physical systems, energy systems, industrial applications, or optimization-adjacent problems. • A desire to build models that are not only accurate, but useful in real software and operational contexts.

Nice to have • Experience with energy systems, industrial systems, or infrastructure-related applications. • Experience with state-space models, dynamical systems, or system identification. • Experience with probabilistic forecasting or uncertainty-aware prediction. • Exposure to control-related applications, MPC, reinforcement learning, or optimization workflows. • Experience taking models beyond prototyping into operational or production-facing use. • Experience working in early-stage startups or small technical teams.

Why Join Us?

This is the kind of role that rarely exists for long: early enough to shape the direction, but grounded enough that your work can land quickly in real contexts.

Local Energy already has active products, real projects, and direct exposure to industrial energy challenges. The models you build will not sit on a shelf. They will help us improve how complex energy systems are designed, simulated, and eventually operated.

You will also contribute to one of our most important technical directions: building safe, predictive, control-oriented intelligence for industrial thermal assets and local energy markets.

If you want to apply machine learning to infrastructure, climate, and the physical world, this is the kind of problem worth working on.

What We Offer • Work directly with a founding team of experienced operators, engineers, and researchers • Competitive compensation and early equity via stock options in a high-upside, mission-driven company • Remote-friendly, flexible work culture built on trust and autonomy • Group insurance policy, with premiums covered in full - including comprehensive health, dental, life and long-term disability coverage • Unlimited vacation – take time off when you need it so you can be at your best • Brand-new MacBook Pro dev machine + monthly stipend for software tools • Access to a comfortable office space at Mila near Montreal’s Little Italy neighbourhood; surrounded by startup energy and top AI talent Apply

Click the link below to open the form and fill out your application 🚀

https://tally.so/r/eq9Y1k