Engineering Manager

You Will:

• Be a people leader of a small (approx 4-6) team of ML and data engineers
• Be hands-on as needed in coding, ML model design, system design, data modelling, code pairing, PR reviews, and writing TDDs (technical design documents)
• Own and drive execution of the technical roadmap for your team in line with the product roadmap
• Provide engineering/technical leadership on Knowledge Enrichment projects that seek to use ML to enrich the data in BenchSci’s Knowledge Graph
• Work closely with other engineering leaders to ensure alignment on technical solutioning
• Liaise closely with stakeholders from other functions including product and science
• Help 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 our recruiting, hiring, and onboarding processes
• Provide mentorship and carry out regular 1:1 meetings with direct reports
• Work with your team to continuously drive improvements in ways of working, productivity and quality of work product

You Have:

• 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
• LLM 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