Machine Learning Engineer

Kraken Digital Asset Exchange
Manchester
1 week ago
Create job alert

Kraken is the operating system for utilities of the future. Built in-house at Octopus Energy, we took them to become the biggest supplier in the UK, and now we power energy companies and utilities around the globe - in 10 countries and counting, licensing software to giants like Origin Energy in Australia and Tokyo Gas in Japan. We’re on a mission to accelerate the renewable transition, and bring affordable green energy to the world.

We’ve reinvented energy products with smart, data-driven tariffs to balance customer demand with renewable generation, and Kraken’s platform controls more than half of the grid-scale batteries in the UK. We’re driving the uptake of low carbon technologies like solar panels and heat pumps via our software for engineers in the field. Our platform allows our energy specialists to be the most productive in the industry, with our suite of AI tools making us pioneers in using ML and AI to make agents’ lives easier and customers happier. We do it by hiring clever, curious, and self-driven people, enabling them with modern tools and infrastructure and giving them lots of autonomy.

Our ML team consists of ML, front-end and back-end engineers, so that we can rapidly prototype and get innovative tools in use at breakneck speed.

We’ve had great success in using AI to bring better service to customers, and we want to bring that success to the whole business. You’ll be part of a small expert team working on the most pressing problems for the business, whether it’s internal AI tooling to make our developers twice as productive, or automating processes to cut months off migration times for new clients. You’ll work across the whole product lifecycle: identifying uses of new technologies via exploration, working closely with teams around the business to validate that your ideas will bring value, and rapidly prototyping. The work you do will define the pattern for AI success at the company.

You’ll have wide open problems to solve, so you’ll need to be comfortable with ambiguity, figuring out an approach and validating it fast. You’ll stay up to date with changes in the field, using your knowledge of state-of-the-art techniques to solve problems. LLMs will be your bread and butter, customized with advanced RAG techniques, finetuning and reinforcement learning. You’ll work closely with other engineers to build fast, and you’ll use Python and Kubernetes to deploy systems in production.

What you'll do
  • Work with a high performance team of LLM, MLOps, backend and front end engineers
  • Tackle the biggest problems facing the company, giving a wide experience across the business, with the freedom to define novel approaches
  • Work to help LLMs understand and interact with the millions of lines of code that run Kraken, leveraging techniques at the cutting-edge of the technology like GraphRAG, agentic workflows, finetuning, and reinforcement learning
  • Use classic ML and NLP techniques to complement and improve LLM systems
  • Act as a center of excellence for the whole business in AI, as a floating resource that consults other teams use of LLMs and lifts the quality of products around the whole business
  • Be on the forefront of understanding AI advancements and their technical implications for the team and business
What you'll need
  • Curious and self driven - in a field that changes so quickly, its essential you have the initiative to make decisions yourself, and can find solutions to novel problems without lots of help and support
  • 1+ year experience with LLMs in production beyond POC and a deep technical understanding of diverse technologies and techniques to adapt LLMs to domains (like advanced RAG techniques, tool calling, finetuning and RL) Of particular interest are cutting-edge AI systems in software engineering, for example working on AI software copilots or autonomous software engineering bots
  • 3+ years experience of traditional ML techniques including training and deploying non-LLM ML models, and ongoing monitoring of production models that incorporate feedback mechanisms to improve
  • A keen interest in Gen AI and classic ML, understanding of emerging trends and research, and proven experience aligning and applying this to real world objectives
It would be great if you had
  • Experience working with large codebases and collaborating with multiple engineering teams in large companies
  • Experience in diverse LLM deployment methods (eg hosted finetuned models via services like Bedrock, and running directly via engines like vLLM)

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 (FR/EN) 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 and we’ll do what we can to customise your interview process for comfort and maximum magic!

Studies have shown that some groups of people, like women, are less likely to apply to a role unless they meet 100% of the job requirements. Whoever you are, if you like one of our jobs, we encourage you to apply as you might just be the candidate we hire. Across Kraken, we’re looking for genuinely decent people who are honest and empathetic. Our people are our strongest asset and the unique skills and perspectives people bring to the team are the driving force of our success. As an equal opportunity employer, we do not discriminate on the basis of any protected attribute. We consider all applicants without regard to race, colour, religion, national origin, age, sex, gender identity or expression, sexual orientation, marital or veteran status,, or any other legally protected status. U.S. based candidates can learn more about their EEO rights here.

Our (i) Applicant and Candidate Privacy Notice and Artificial Intelligence (AI) Notice, (ii) Website Privacy Notice and (iii) Cookie Notice 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, but not limited to, the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (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.


#J-18808-Ljbffr

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

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.

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.