Machine Learning Engineer - Utilities
Machine Learning Engineer - Utilities

Machine Learning Engineer - Utilities

Full-Time 36000 - 60000 £ / year (est.) No home office possible
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At a Glance

  • Tasks: Design and improve machine learning features for live energy systems.
  • Company: Join Kraken, a tech leader transforming the energy industry sustainably.
  • Benefits: Flexible hybrid work, competitive salary, and a supportive culture.
  • Why this job: Make a real impact on a sustainable future with cutting-edge technology.
  • Qualifications: Strong Python and SQL skills, plus a foundation in machine learning.
  • Other info: Collaborative environment with opportunities for growth and innovation.

The predicted salary is between 36000 - 60000 £ per year.

Help us use technology to make a big green dent in the universe! Kraken powers some of the most innovative global developments in energy. We’re a technology company focused on creating a smart, sustainable energy system. From optimising renewable generation, creating a more intelligent grid and enabling utilities to provide excellent customer experiences, our operating system for energy is transforming the industry around the world in a way that benefits everyone. It’s a really exciting time in energy. Help us make a real impact on shaping a better, more sustainable future.

This is a hands-on, delivery-focused ML engineering role. You’ll join a team working on mature, live products where the priority is shipping, improving, and maintaining features. While the role is ML-focused, our ML engineers work very close to the software layer so being comfortable with production Python code, databases, and systems is essential. You’ll initially focus on the Companion product, a RAG based chatbot, supporting feature delivery, maintenance, and incremental improvements, while collaborating closely with product managers and engineers across Kraken.

What you'll do:

  • Design, build, and improve machine learning and GenAI-powered features used in live production systems
  • Deliver consistent, high-quality work each sprint (typically 2–3 smaller tickets or 1 larger piece of work per two-week sprint)
  • Work with product managers to clarify requirements and translate them into robust technical solutions
  • Write clean, maintainable Python code and contribute to shared codebases used across ML teams
  • Analyse data, evaluate approaches, and iterate on solutions based on real-world usage
  • Collaborate with other ML engineers and software engineers across Kraken when working on shared systems
  • Ask questions early, seek clarification when needed, and contribute ideas during team discussions
  • Participate in sprint planning, stand-ups, and knowledge-sharing sessions

While our current products are largely GenAI-based and do not train models, a strong grounding in ML fundamentals is still important as the platform continues to evolve.

What you'll need:

  • A solid foundation in machine learning fundamentals, including data analysis, model evaluation, and ML pipelines
  • Strong experience with Python and SQL in a production environment
  • Comfort working in software-engineering-heavy ML roles (this is not a research-only position)
  • Experience working with real-world systems where reliability, readability, and maintainability matter
  • Confidence asking questions, collaborating across teams, and explaining your thinking
  • Ability to work independently on defined tasks and see them through to completion

Experience with the following is a bonus:

  • Exposure to GenAI / LLM-based systems (e.g. prompting, orchestration, evaluation)
  • Familiarity with cloud environments (especially AWS)
  • Experience with tools such as Databricks, Datadog, or similar data / observability platforms
  • Awareness of ML libraries such as PyTorch, TensorFlow, or Hugging Face (even if not used day-to-day)

Ways of working:

  • Two-week sprints with planning and retrospectives
  • Asynchronous-first communication with daily stand-ups
  • Regular knowledge-sharing sessions
  • A no-blame culture with high trust and autonomy
  • Flexible hybrid working, with in-person collaboration typically on Tuesdays and Thursdays

We would prefer someone who can work in our London office on a hybrid remote policy of 1-2 days a week onsite. You do need to be able to work in the UK. We're very excited to be growing our team. We're looking for skills and experience to help shape and define the future of not only our team, but the wider business at a global scale. If you're reading this and grinning, please apply! There are huge challenges to tackle, and we need amazing people who are keen to get stuck in.

Kraken is a certified Great Place to Work in France, Germany, Spain, Japan and Australia. In the UK we are one of the Best Workplaces on Glassdoor with a score of 4.7. Check out our Welcome to the Jungle site to learn more about our teams and culture.

Are you ready for a career with us? We want to ensure you have all the tools and environment you need to unleash your potential. If you have any specific accommodations or a unique preference, please contact us at inclusion@kraken.tech and we'll do what we can to customise your interview process for comfort and maximum magic!

Our Applicant and Candidate Privacy Notice and related policies govern the collection and use of your personal data in connection with your application and use of our website. These policies explain how we handle your data and outline your rights under applicable laws, including GDPR and CCPA. Depending on your location, you may have the right to access, correct, or delete your information, object to processing, or withdraw consent. By applying, you acknowledge that you've read, understood and consent to these terms.

Machine Learning Engineer - Utilities employer: Kraken Digital Asset Exchange

At Kraken, we are committed to fostering a dynamic and inclusive work environment where innovation thrives. As a Machine Learning Engineer in our London office, you'll be part of a forward-thinking team dedicated to transforming the energy sector through cutting-edge technology. With flexible hybrid working arrangements, a strong focus on employee growth, and recognition as one of the Best Workplaces on Glassdoor, Kraken offers a unique opportunity to make a meaningful impact while advancing your career in a supportive culture.
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Contact Detail:

Kraken Digital Asset Exchange Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Machine Learning Engineer - Utilities

✨Tip Number 1

Get to know the company inside out! Research Kraken's projects, values, and culture. This will help you tailor your conversations and show that you're genuinely interested in making a difference in the energy sector.

✨Tip Number 2

Network like a pro! Connect with current employees on LinkedIn or attend industry events. Building relationships can give you insider info and might even lead to a referral, which is always a bonus!

✨Tip Number 3

Prepare for technical interviews by brushing up on your Python and ML fundamentals. Be ready to discuss your past projects and how you've tackled real-world problems. Practice coding challenges to keep your skills sharp!

✨Tip Number 4

Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re serious about joining our team and contributing to a sustainable future.

We think you need these skills to ace Machine Learning Engineer - Utilities

Machine Learning Fundamentals
Data Analysis
Model Evaluation
ML Pipelines
Python
SQL
Software Engineering
Collaboration Skills
Problem-Solving Skills
GenAI / LLM Exposure
Cloud Environments (AWS)
Databricks
Datadog
ML Libraries (PyTorch, TensorFlow, Hugging Face)

Some tips for your application 🫡

Show Your Passion for Sustainability: When writing your application, let your enthusiasm for creating a sustainable energy system shine through. We want to see how your skills can contribute to making a big green dent in the universe!

Highlight Relevant Experience: Make sure to showcase your hands-on experience with machine learning and Python. We’re looking for someone who’s comfortable working close to the software layer, so don’t hold back on those production examples!

Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so make it easy for us to see how you meet the requirements and what you can bring to our team.

Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity with Kraken!

How to prepare for a job interview at Kraken Digital Asset Exchange

✨Know Your ML Fundamentals

Brush up on your machine learning basics, especially data analysis and model evaluation. Being able to discuss these concepts confidently will show that you have a solid foundation, which is crucial for the role.

✨Showcase Your Python Skills

Prepare to demonstrate your Python coding abilities. You might be asked to solve a problem or explain your approach to writing clean, maintainable code. Practising common coding challenges can help you feel more at ease.

✨Understand the Product

Familiarise yourself with Kraken's Companion product and its features. Being able to discuss how you would improve or maintain it shows that you're proactive and genuinely interested in the company's work.

✨Ask Questions

Don’t hesitate to ask questions during the interview. This not only clarifies your understanding but also demonstrates your collaborative spirit. Remember, they value communication and teamwork, so showing your willingness to engage is key!

Machine Learning Engineer - Utilities
Kraken Digital Asset Exchange

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