Machine Learning Engineer – Utilities
Kraken
full-time
Posted on:
Location Type: Hybrid
Location: London • United Kingdom
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Job Level
About the role
- 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
Requirements
- 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)
Benefits
- Flexible hybrid working, with in-person collaboration typically on Tuesdays and Thursdays
- Regular knowledge-sharing sessions
- A no-blame culture with high trust and autonomy
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningdata analysismodel evaluationML pipelinesPythonSQLGenAILLM-based systemscloud environmentsML libraries
Soft Skills
collaborationcommunicationindependenceproblem-solvingadaptabilityclarification seekingidea contributionteam discussionssprint planningknowledge sharing