We are looking for an Engineering Manager to join our Knowledge Enrichment team. You will be reporting to the Director of Engineering, Data & ML. In this impactful role, you will work closely with key stakeholders across the organisation and be instrumental in cross-team priorities and management. The most successful candidates for this role will be experienced ML engineers who have recently transitioned to leading ML engineer ICs, and delivering complex ML engineering solutions. This role is perfect for a leader who is technically adept and passionate about guiding a team toward innovative solutions in machine learning and data engineering. The successful candidate will be not only a technical leader but also a mentor, coach, and role model in our organization.
You Will:
1. Be a people leader of a small (approx 4-6) team of ML and data engineers
2. Be hands-on as needed in coding, ML model design, system design, data modelling, code pairing, PR reviews, and writing TDDs (technical design documents)
3. Own and drive execution of the technical roadmap for your team in line with the product roadmap
4. Provide engineering/technical leadership on Knowledge Enrichment projects that seek to use ML to enrich the data in BenchSci’s Knowledge Graph
5. Work closely with other engineering leaders to ensure alignment on technical solutioning
6. Liaise closely with stakeholders from other functions including product and science
7. Help ensure adoption of ML best practices and state of the art ML approaches at BenchSci
8. Drive agile practices within the team, and lead certain agile rituals
9. Take a leadership role in our recruiting, hiring, and onboarding processes
10. Provide mentorship and carry out regular 1:1 meetings with direct reports
11. Work with your team to continuously drive improvements in ways of working, productivity and quality of work product
You Have:
12. 5+ years of experience working as a professional ML engineer
13. 3+ years in technical leadership roles
14. 2+ years of experience working as an ML engineering manager
15. Technical focus: have remained technically hands-on and have regularly contributed code over the last 12 months
16. Technical leadership: a proven track record of delivering complex ML projects with high-performing teams leveraging state-of-the-art ML techniques
17. ML proficiency: deep understanding of modern machine learning techniques and applications
18. ML frameworks/libs: Mastery of several ML frameworks and libraries, with the ability to architect complex ML systems from scratch
19. ML model deployment: expert in training, fine-tuning, and deploying machine learning models at scale, with a focus on optimising performance and efficiency
20. LLM acumen: strong skills in implementing Large Language Models. Deep understanding of the Retrieval Augmented Generation architecture and ideally deploying solutions leveraging RAG
21. GML/GNNs: expertise in graph machine learning/graph neural networks and practical applications
22. Technical expertise: Comprehensive knowledge of software engineering and industry experience using Python
23. Domain: ideally worked in the biological/science domain
24. Agile practices: well-versed in Agile software development methodologies
25. Effective communication: outstanding verbal and written communication skills. Can clearly explain complex technical concepts/systems to engineering peers and non-engineer stakeholders
26. Growth mindset: up-to-date with cutting-edge advances in ML/AI, actively engaging with the community