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Engineering Manager - Knowledge Enrichment

fully flexible

Remote:

Full Remote

Contract:

Full time

Experience:

Senior (5-10 years)

Work from:

United Kingdom

Offer summary

Qualifications:

5+ years working as an ML engineer, 3+ years in technical leadership roles, 2+ years as an ML engineering manager, Mastery of ML frameworks and libraries, Strong skills in Large Language Models.

Key responsabilities:

Lead a small team of ML engineers
Drive execution of the team's technical roadmap
Ensure adoption of ML best practices
Mentor team members and facilitate 1:1 meetings
Participate in recruiting and onboarding processes

BenchSci Scaleup https://www.benchsci.com/

201 - 500 Employees

See more BenchSci offers

Job description

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