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Salary: GBP 75,000 - 200,000 / annual
Axle Energy is building software infrastructure for the decarbonised energy system. The company controls hundreds of thousands of energy assets—vehicle charging systems, heating systems, and home batteries—optimizing when energy is consumed to align with cheap and green electricity availability. Backed by Energize Capital and Accel, Axle operates at high speed in a legacy industry, moving real gigawatt-hours of electrons while scaling rapidly to meet customer demand.
In this role, you will be a full-stack data scientist owning the complete lifecycle of your work: from data exploration and research through prototyping to production deployment. Key responsibilities include:
- Building, optimizing, and deploying machine learning models that operate reliably at scale
- Developing deep domain expertise in electricity markets, grid dynamics, and trading strategies
- Writing clean, production-quality Python code and contributing to robust, scalable systems
- Moving beyond notebooks to turn research ideas into real-world, productionized solutions
- Collaborating across a small, flat-structured team and occasionally engaging with clients
The tech stack centers on Python for backend work, React for frontend (less relevant here), Docker for deployment, and Google Cloud Platform (GCP) for infrastructure. The team is an aggressive user of AI tools like Claude Code and encourages continuous workflow optimization.
Axle operates with a deliberately flat structure and equitable pay philosophy (1:1 median ratio between founder and team compensation). The London office near Farringdon is dog-friendly, and the role is hybrid with an expectation of 2–3 days per week in-office to maximize collaboration. Additional benefits include meaningful equity, enhanced parental leave, and bi-annual team retreats.
REQUIREMENTS:
- Knowledge of the electricity system, specifically power trading (nice-to-have)
- Comfort speaking to clients and wearing multiple hats in a small team (nice-to-have)
- Familiarity with time-series data (nice-to-have)
- Proficiency in Python and ability to write production-quality code
- Experience building and deploying machine learning models at scale
- Ability to own projects end-to-end from research to production
