Engineering Manager - Knowledge Enrichment

September 13

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BenchSci

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Antibody • Research • Machine Learning • Online Platform • Science

201 - 500

💰 Series C on 2022-01

Description

• 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

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 • 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