Senior Machine Learning Engineer
Upload My Resume
Drop here or click to browse · PDF, DOCX, DOC, RTF, TXT
Requirements
• ~3 years of professional experience as a Machine Learning Engineer or similar applied ML role. • Strong Python skills and experience with common ML libraries and frameworks. • Practical experience taking ML models from development into production. • Good understanding of software engineering fundamentals (version control, testing, CI/CD etc).Experience working with cloud infrastructure and data pipelines. • An ability to explain ML concepts clearly to non-ML engineers.A bias towards action, learning quickly, and improving systems over time. • Prior experience building internal platforms or shared tooling. • Exposure to MLOps practices, including model monitoring, evaluation, and deployment automation. • Familiarity with considerations regarding data privacy, security, or responsible AI. • 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 4.5 and in Germany we rate 4.7 on Kununu as a Top Company. 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 [email protected] and we'll do what we can to customise your interview process for comfort and maximum magic!
Responsibilities
• This role differs from product-embedded ML roles - your focus will be foundational capability, not a single feature or model. • Build and maintain the foundational AI gateways and inference services used across Kraken to provide reliable and efficient access to ML and generative AI models. • Architect and evolve internal evaluation tooling and monitoring frameworks that allow teams to measure the performance, quality, and safety of their systems at scale. • Act as a technical mentor by teaching software engineers and ML specialists how to adopt foundational capabilities, ensuring AI is easy to use and integrated into everyday development. • Create and maintain high-quality documentation, internal guidance, and technical standards to help teams understand when and how to use AI effectively. • Continuously improve Kraken's approach to AI enablement by balancing speed, cost, and quality within the infrastructure you manage. • Work pragmatically - favouring solutions that work in production over theoretical perfection.