Machine Learning Engineer
Description For Machine Learning Engineer
Kraken, the innovative operating system for utilities of the future, is seeking a Machine Learning Engineer to join their dynamic team. Built by Octopus Energy, Kraken has become a global force in the utilities sector, operating in 10 countries and partnering with major energy providers like Origin Energy and Tokyo Gas. The role offers a unique opportunity to work at the intersection of renewable energy and cutting-edge AI technology.
As a Machine Learning Engineer, you'll be part of a specialized team working on mission-critical problems across the business. Your work will involve leveraging advanced LLM techniques, including GraphRAG, agentic workflows, finetuning, and reinforcement learning, to help AI systems understand and interact with millions of lines of code. You'll be instrumental in developing AI tooling that enhances developer productivity and automates complex processes.
The position requires a blend of technical expertise and innovative thinking, with opportunities to work across the entire product lifecycle. You'll be collaborating with a high-performance team of LLM, MLOps, backend, and frontend engineers, while serving as an AI center of excellence for the whole business. The role demands someone who's comfortable with ambiguity and capable of rapid prototyping and validation.
This is an ideal opportunity for a machine learning professional with 3+ years of experience who wants to make a significant impact in the renewable energy sector while working with cutting-edge AI technologies. The position offers the chance to shape the future of AI implementation within a rapidly growing global company, with the freedom to define novel approaches to complex challenges.
Responsibilities For Machine Learning Engineer
- Work with a high performance team of LLM, MLOps, backend and front end engineers
- Tackle the biggest problems facing the company across the business
- Help LLMs understand and interact with millions of lines of code using 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 AI across the business
- Stay current with AI advancements and their technical implications
Requirements For Machine Learning Engineer
- 1+ year experience with LLMs in production beyond POC
- Deep technical understanding of LLM adaptation techniques
- 3+ years experience with traditional ML techniques
- Experience in training and deploying non-LLM ML models
- Understanding of emerging AI trends and research
- Curious and self-driven personality
- Experience with production model monitoring and feedback mechanisms
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