Market Expansion

Axle Energy
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist - Drive Strategy & Growth

Data Analyst

Data Engineer

Machine Learning Engineer

Postdoctoral Fellow- Computational Biology and Machine Learning

Market Data & Reference Data Analyst (Contract)

The electricity grid is changing beyond recognition, and without deploying new software to orchestrate it, we’ll be unable to decarbonise.

At Axle, we’re building the infrastructure that’ll underpin the decarbonised energy system. Our software crushes CO2 and energy costs. Our goal is insanely ambitious, and we’re building a team to match the scale of this challenge. We’ve just raised a Seed round from world-leading investors including Accel (TechCrunch) and we’re growing fast.

We make the technology to move energy usage to times when electricity is cheap and green. Our software controls vehicle charging, heating systems, and home batteries. We use machine learning to figure out what energy people will need, and when they'll need it. We control tens of thousands of energy assets, and we’re growing quickly.

Axle is a unique startup. We’re building in a legacy industry and moving gigawatt-hours of electrons in the real world, but we operate at lightning speed. We ship extraordinarily quickly, and we’re experts in electricity systems. We’re backed by some of the best investors in the world, and we’re growing the team to meet customer demand.

Read more about what we’re buildinghere.

Requirements

This role is responsible for Axle’s participation in energy and flexibility markets, in the UK and globally. This includes:

  • figuring out which flexibility markets we should be participating in, and staying up-to-date on changes in the market
  • entering new flexibility markets in the UK and globally
  • defining the product requirements for market participation


This is a technical and domain-heavy role, but the relevant domain knowledge can be learned. To succeed in this role you’ll need the interest & ability to absorb & process highly-technical information, the foresight to design & execute long-term projects (and course-correct when necessary), and a ‘run through walls’ attitude to overcome the barriers to get us into markets at a startup-urgent pace.

You can expect:

  • insane amounts of ownership
  • hard technical challenges
  • that what you build is commercially and environmentally valuable

In return, we ask for:

  • the courage to to build new things fast
  • a commitment to real world impact over perfection
  • a desire to help build and lead an exceptional and tight knit team
  • deep-seated motivation to combat climate change

This role is an opportunity to repeatedly deliver first-of-a-kind projects across the world, to become an expert in electricity markets globally, and to work at the bleeding edge of global electrification.

Interview process

  1. Initial interview
  2. Take-home exercise
  3. Reference call
  4. Final interview (in-person)
  5. Offer, add'l references, and welcome to the team!

Benefits

We love the idea of fully remote work but it doesn't… work. For very early stage companies, people learn faster, get on better, and accomplish more when they're spending a decent chunk of time together. We ask that you spend 2-3 days a week in our London office.

We areextremelykeen to build a diverse company, and we’re particularly eager to hear from candidates who don't fit the traditional role stereotypes. If you’re motivated by our mission, please do reach out, even if you feel you might not ‘check all the boxes’.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Jobs

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.