Machine Learning Engineer

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. Were on a mission to accelerate the renewable transition and bring affordable green energy to the world.

Weve reinvented energy products with smart data driven tariffs to balance customer demand with renewable generation and Krakens platform controls more than half of the grid-scale batteries in the UK. Were 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.

What youll 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 youll 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)