Analyse and manipulate a large, highly connected biological knowledge graph constructed of data from multiple heterogeneous sources, in order to identify data enrichment opportunities and strategies.
Work with data and knowledge engineering experts to design and develop knowledge enrichment approaches/strategies that can exploit data within our knowledge graph.
Provide solutions related to classification, clustering, more-like-this-type querying, discovery of high-value implicit relationships, and making inferences across the data that can reveal novel insights.
Deliver robust, scalable, and production-ready ML models, with a focus on optimizing performance and efficiency.
Architect and design ML solutions, from data collection and preparation, model selection, training, fine-tuning, and evaluation, to deployment and monitoring.
Collaborate with your teammates from other functions such as product management, project management, and science, as well as other engineering disciplines.
Sometimes provide technical leadership on Knowledge Enrichment projects that seek to use ML to enrich the data in BenchSci’s Knowledge Graph.
Work closely with other ML engineers to ensure alignment on technical solutions and approaches.
Liaise closely with stakeholders from other functions including product and science.
Help ensure the adoption of ML best practices and state-of-the-art ML approaches at BenchSci.
Participate in and sometimes lead various agile rituals and related practices.
Requirements & Skills:
Minimum 5, ideally 8+ years of experience working as an ML engineer.
Minimum 1, ideally 3+ years of technical leadership experience, including leading 5-10 ICs technically on complex projects
Degree, preferably PhD, in Software Engineering, Computer Science, or a similar area.A proven track record of delivering complex ML projects working alongside high performing ML engineers using agile software development.
Demonstrable ML proficiency with a deep understanding of how to utilise state-of-the-art NLP and ML techniques.
Mastery of several ML frameworks and libraries, with the ability to architect complex ML systems from scratch. Extensive experience with Python and PyTorch.
Track record of successfully delivering robust, scalable, and production-ready ML models, with a focus on optimizing performance and efficiency.
Experience with the full ML development lifecycle from architecture and technical design, through data collection and preparation, model selection, training, fine-tuning and evaluation, to deployment and maintenance.
Strong skills related to implementing solutions leveraging Large Language Models, as well as a deep understanding of how to implement solutions using Retrieval Augmented Generation (RAG) architecture.
Expertise in graph machine learning (i.e. graph neural networks, graph data science) and practical applications thereof. This is complemented by your experience working with Knowledge Graphs, ideally biological, and familiarity with biological ontologies.
Experience with complex problem solving and an eye for details such as scalability and performance of a potential solution.
Comprehensive knowledge of software engineering, programming fundamentals, and industry experience using Python.
Experience with data manipulation and processing, such as SQL, Cypher or Pandas.
A can-do proactive and assertive attitude – your manager believes in freedom and responsibility and helping you own what you do; you will excel best if this environment suits you.
You have experience working in cross-functional teams with product managers, project managers, engineers from other disciplines (e.g. data engineering).Ideally you have worked in the scientific/biological domain with scientists on your team.
Outstanding verbal and written communication skills. Can clearly explain complex technical concepts/systems to engineering peers and non-engineering stakeholders.
A growth mindset continuously seeking to stay up-to-date with cutting-edge advances in ML/AI, complimented by actively engaging with the ML/AI community.
