Senior Machine Learning Engineer - Graph ML

You Will:

• Analyze 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 optimising 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 solutioning and approaches.
• 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 within your team(s).Participate in various agile rituals and related practices

You Have:

• Minimum 3, ideally 5+ years of experience working as an ML engineer
• Some experience providing technical leadership 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, data, and software engineers using agile software development
• Demonstrable ML proficiency with a deep understanding of how to utilize 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 contributing to the successful delivery of robust, scalable and production-ready ML models, with a focus on optimising 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
• Familiarity with implementing solutions leveraging Large Language Models, as well as a deep understanding of how to implement solutions using Retrieval Augmented Generation (RAG) architecture
• Experience with graph machine learning (i.e. graph neural networks, graph data science) and practical applications thereof
• This is complimented by your experience working with Knowledge Graphs, ideally biological, and a 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, scientists, 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