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ML Engineering Manager

2 months ago

Cambridge, Cambridgeshire, United Kingdom BenchSci Full time
About the Role

We are seeking an experienced ML Engineering Manager to join our team at BenchSci. As a key member of our Knowledge Enrichment team, you will be responsible for leading a small team of ML and data engineers in delivering complex ML engineering solutions.

Key Responsibilities
  • Lead a team of 4-6 ML and data engineers in the development of ML models and systems.
  • Drive the technical roadmap for the team, ensuring alignment with the product roadmap.
  • Provide technical leadership on Knowledge Enrichment projects, leveraging state-of-the-art ML techniques.
  • Collaborate with other engineering leaders to ensure technical solutioning alignment.
  • Liaise with stakeholders from product and science functions.
  • Ensure adoption of ML best practices and state-of-the-art ML approaches at BenchSci.
  • Drive agile practices within the team and lead certain agile rituals.
  • Take a leadership role in recruiting, hiring, and onboarding processes.
  • Provide mentorship and conduct regular 1:1 meetings with direct reports.
  • Work with the team to drive improvements in ways of working, productivity, and quality of work product.
Requirements
  • 5+ years of experience working as a professional ML engineer.
  • 3+ years in technical leadership roles.
  • 2+ years of experience working as an ML engineering manager.
  • Technical focus: have remained technically hands-on and have regularly contributed code over the last 12 months.
  • Technical leadership: a proven track record of delivering complex ML projects with high-performing teams leveraging state-of-the-art ML techniques.
  • ML proficiency: deep understanding of modern machine learning techniques and applications.
  • ML frameworks/libs: Mastery of several ML frameworks and libraries, with the ability to architect complex ML systems from scratch.
  • ML model deployment: expert in training, fine-tuning, and deploying machine learning models at scale, with a focus on optimising performance and efficiency.
  • LMM acumen: strong skills in implementing Large Language Models. Deep understanding of the Retrieval Augmented Generation architecture and ideally deploying solutions leveraging RAG.
  • GML/GNNs: expertise in graph machine learning/graph neural networks and practical applications.
  • Technical expertise: Comprehensive knowledge of software engineering and industry experience using Python.
  • Domain: ideally worked in the biological/science domain.
  • Agile practices: well-versed in Agile software development methodologies.
  • Effective communication: outstanding verbal and written communication skills. Can clearly explain complex technical concepts/systems to engineering peers and non-engineer stakeholders.
  • Growth mindset: up-to-date with cutting-edge advances in ML/AI, actively engaging with the community.