Optimization Engineer – Revenue Distribution Focus

Capalo AI Oy; Helsinki; Hybrid; EUR 42,000 - 56,000; Full time

Job summary

Capalo AI is seeking a Junior Optimization Engineer in Helsinki to join the Revenue Distribution team. You will validate and stress-test the allocation engine that distributes aggregated market revenues across a fleet of battery assets, ensuring fairness, robustness, and auditability.

You will work on production-grade Python modules, integrate outputs with core systems, and help evolve allocation methodologies as markets and the fleet grow.

Qualifications

• Completed or soon-to-be completed degree in a quantitative field such as applied mathematics, physics, optimization, operations research, or quantitative economics
• Strong academic track record
• Solid Python skills (NumPy, pandas) and familiarity with testing and version control; 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

Responsibilities

• Validate and stress-test the allocation engine for fairness, robustness, and edge cases
• Integrate API-ready outputs with Finance and customer-facing products
• Maintain and extend production-grade Python modules
• Handle large-scale time-series market data and write well-documented, testable code
• Refine revenue-sharing mechanisms and formally define contribution logic under portfolio-level optimization

Skills

Python

NumPy

Pandas

Testing

Education

Degree in applied mathematics or similar

Tools

SciPy

CVXPy

Job description

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 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 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 (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.

It’s close to real markets.

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