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 design and implement the mathematical framework that determines how aggregated market revenues are allocated back to individual battery assets in our fleet. This is an entry-level position, well suited to recent graduates and students in the final stages of their degree.
This 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 to formalize that logic and ship it.
Key Responsibilities
Design allocation methodologies
Develop revenue-sharing mechanisms for heterogeneous battery portfolios
Account for asset constraints, degradation, market rules, and temporal coupling
Formalize contribution logic under portfolio-level optimization
Build production-grade optimization logic
Implement high-performance Python modules
Handle large-scale time-series market data
Write testable, well-documented, reviewable code
Ensure numerical correctness and edge-case robustness
Validate and stress-test
Simulate volatile price environments
Evaluate fairness metrics and sensitivity
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
What we're looking forMust-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 (NumPy, pandas) and familiarity with good software practices such as testing and version control, e.g. through coursework, projects or internships; exposure to SciPy, 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.

Work arrangement
No