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:
- 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
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Last updated on Sep 12, 2024