Energy Optimization & ML Engineer
Help build the energy system of the future
Help energy communities make the most of local renewable energy while staying within the limits of the electricity grid. At WePositive Energy, we’re looking for an Energy Optimization & ML Engineer who combines practical energy-domain experience with optimisation and modelling skills—and wants to put them to work building software. You will be working on new challenges and software as well as getting our existing stack production ready.
This is a hands-on Python software engineering role, in which you will be working as part of an experienced team of software engineers and energy experts, and in close collaboration with our research and industry partners. Experience with machine learning or data science is a welcome addition, but energy-system understanding and optimisation are at the heart of the role.
What you’ll be trusted with?
Develop algorithms that help homes and communities use and share locally generated energy, coordinating batteries, solar generation, EV charging and other flexible demand.
Model energy flows, asset flexibility and operational constraints, including battery state of charge, charging and discharging efficiencies, and grid import/export limits.
Build optimisation approaches that shift consumption and storage in time to reduce grid-capacity peaks, increase local renewable-energy use and balance community and household needs.
Choose pragmatic solutions—from rules and heuristics to linear and mixed-integer linear programming (LP/MILP)—and explain the trade-offs between model accuracy, feasibility and computation time.
Turn prototypes and notebooks into maintainable Python code, integrating optimisation algorithms into our backend services and coordinating how they run and how their results reach energy-management systems.
Work with measured and forecast time-series data, validate assumptions through simulations and tests, and handle incomplete data, changing forecasts and infeasible schedules sensibly.
Take ownership beyond the model: test, document, deploy, monitor and improve the software you build, together with our engineering team.
Where your skills and interests fit, contribute to forecasting and other data-driven improvements to our platform.
What will you bring?
Relevant hands-on experience with energy systems, such as energy management, battery storage, smart grids, demand flexibility or electricity markets. You understand the physical and operational realities behind the data.
Practical experience modelling and optimising energy systems: translating a real-world problem into objectives, decision variables and constraints, and checking whether the result makes sense.
Experience with mathematical optimisation, including LP/MILP, and the judgement to use a simpler approach when it is sufficient.
The ability to build useful software in Python, work with time-series data and read and extend an existing codebase. Experience with Pandas or Polars is valuable.
A genuine desire to work as a software engineer: building features, debugging, writing tests and maintaining existing systems—not only developing new models.
A commitment to clean, maintainable code and a willingness to develop your skills in testing, type hints, Git, code reviews and production software design. You don’t need to arrive as an expert in every part of our stack; you do need to enjoy learning from other engineers.
Clear communication, curiosity and a collaborative working style. You ask questions, challenge assumptions and seek feedback rather than working in isolation.
A growth mindset and the motivation to make a tangible contribution in a small, ambitious sustainable-energy startup.
Nice to have:
Machine learning or data science experience, particularly energy time-series forecasting, anomaly detection or model validation.
Familiarity with Dutch or European electricity markets, tariffs and grid-congestion challenges.
Experience with APIs, backend services, Docker, cloud deployments or integrating optimisation models into operational energy-management systems.
Tech Stack
You’ll work with our Python-based, cloud-native platform:
Python, pytest, type hints and asyncio
Polars and Jupyter notebooks for time-series analysis and experimentation
FastAPI and event-driven microservices
PostgreSQL with TimescaleDB for time-series data
AWS, Docker, Terraform and GitHub Actions
Notion for collaborative documentation and planning
Github copilot with all major frontier models
What do we offer?
A hands-on role at the intersection of software engineering, energy-system modelling and optimisation, with real impact on the energy transition.
The opportunity to see your algorithms become part of a product used to manage real energy systems—not stop at a prototype or research report.
Close collaboration with experienced software engineers and energy-domain experts, with room to strengthen your engineering skills and share your own expertise.
Meaningful ownership and influence over our algorithms and technical direction in a small, ambitious team.
A remote-first culture that balances autonomy with collaboration and occasional in-person meetings in the Netherlands.
Flexible working arrangements and a collaborative development process following Shape Up.
Potential to own shares in the company in the future.
Interview process
Getting to know each other: discuss your experience, motivation and what you are looking for in your next role.
Technical interview and practical assessment: explore your Python skills and approach to modelling and optimisation through realistic battery and community-energy scenarios. We’re interested in your reasoning, assumptions and engineering judgement—not knowledge of our internal terminology.
Final conversation: meet the founders, discuss the role and mutual expectations, and agree on conditions.
Ready to apply? Click the button below to fill out our application form.