PA
PartSpaceMachine Learning Engineer – m/f/d
Machine Learning Engineer responsible for optimizing AI pipelines at Easy2Parts. Join a growing team to revolutionize component sourcing with AI technology.
About the role
- Work closely with ML engineering teams and SREs to manage infrastructure, observability, deployment and management of in-production ML models
- Collaborate with ML researchers to refactor and productionize their models and algorithms to meet performance, reliability, and maintainability standards
- Streamline and optimise deployment pipelines to enable fast and reliable delivery of new ML models
- Develop challenger/champion testing frameworks and automated tests
- Optimise infrastructure to reduce cost and increase reliability
- Monitor model and infrastructure performance and manage alerting and integration with business processes
- Work with the AI and decisioning team and Director of AI and Decision Intelligence to input into choices on objective functions, content strategy and wider data strategy to ensure good long-term ML outcomes
Requirements
- Experience deploying production-ready solutions into production, serving at scale of millions of users
- Experience refactoring and productionizing ML models and algorithms
- Experience with deployment pipelines and optimisation for fast, reliable delivery of ML models
- Experience developing challenger/champion testing frameworks and automated tests
- Experience optimising infrastructure to reduce cost and increase reliability
- Experience monitoring model and infrastructure performance and managing alerting and integrations with business processes
- Familiarity with Databricks on Azure and deploying on Databricks or Azure Kubernetes Service (AKS)
- Systems thinking and ability to design scalable solutions
- Optimisation mindset
- Ability to thrive in a fast-paced startup environment and work with ambiguity
- Eager to learn and challenge existing frameworks
- Must be based in the United Kingdom (role requires UK-based candidates)
- No visa sponsorship available; cannot consider overseas applications
Benefits
- Hybrid working: 2 days in Moneybox London office and 3 from home
- Commitment to inclusion, diversity and equity; adjustments available during recruitment process
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
NO
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