Junior Optimization Engineer – Revenue Distribution Focus

About Capalo AI At Capalo AI, we are accelerating the green energy transition by maximizing the value of large-scale energy storage systems through artificial intelligence and optimization. Our platform, Capalo Zeus VPP™, operates battery energy storage systems as a virtual power plant, helping asset owners generate higher revenues while supporting a more resilient and sustainable energy grid.

The role As a Junior Optimization Engineer within our Revenue Distribution team, you’ll work on the allocation engine that distributes aggregated market revenues back to the individual battery assets in our fleet. T his is a quantitative systems problem. When hundreds of batteries are optimized as one portfolio, their individual contributions are not directly observable.

Yet revenues must be allocated in a way that is: Economically fair Mathematically defensible Robust to edge cases Transparent to asset owners Production-grade Your job is making sure the engine runs correct, validating and stress-testing its outputs, integrating it with our core systems, and adapting it as market rules and our fleet change.

You'll also help improve the allocation methods themselves. This is an entry-level position, well suited to recent graduates and students in the final stages of their degree.

Key Responsibilities

Validate and stress-test Evaluate fairness metrics and sensitivity Ensure numerical correctness and robustness to edge cases Adapt models as market rules evolve Integrate with core systems Deliver API-ready outputs used by Finance and customer-facing products Maintain documentation to ensure auditability and transparency Maintain and extend production code Maintain and improve high-performance Python modules Handle large-scale time-series market data Write testable, well-documented, reviewable code Improve allocation methodologies Refine revenue-sharing mechanisms for heterogeneous battery portfolios Account for asset constraints, degradation, market rules and temporal coupling Formalize contribution logic under portfolio-level optimization What we're looking for Must-haves A completed or soon-to-be-completed degree in applied mathematics, physics, optimization, operations research, quantitative economics, or a similar quantitative field A strong academic track record Solid Python skills (Num.

Py, pandas) and familiarity with good software practices such as testing and version control, e.g. through coursework, projects or internships; exposure to Sci. Py, CVXPy or similar tools is a plus Ability to break down open-ended quantitative problems in a structured way, and eagerness to learn quickly Clear communication skills in English - able to explain mathematical reasoning to non-specialists Nice-to-haves Exposure to electricity markets or battery energy storage systems (BESS), e.

g. through studies, a thesis or projects Coursework or projects involving revenue or cost allocation in multi-asset environments Studies in mechanism design or cooperative game theory Why this role is different You’re building core logic, not peripheral features. The allocation engine directly determines how real money is distributed.

It’s close to real markets. The technology runs against live electricity markets and has real-world impact. The problem deepens over time. As we scale across markets and asset classes, allocation complexity increases. Why Capalo AI Work on technology that is accelerating the transition to renewable energy Join a fast-growing company at the intersection of AI and energy markets Work with a highly collaborative and mission-driven team Interesting technical challenges: distributed systems, event-driven cloud services, and reliable software for continuously operating infrastructure What to expect Location: Finland Working mode: Hybrid Recruitment process: First interview → Take-home assignment → Technical interview → Bar raiser How to apply If this sounds like a problem you'd enjoy solving, we'd like to hear from you.

We value diverse perspectives and encourage candidates from all backgrounds to apply. We review applications on a rolling basis.