Lead Machine Learning Engineer

You Will:

• 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 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 at BenchSci
• Participate in and sometimes lead various agile rituals and related practices

You Have:

• Minimum 5, ideally 8+ years of experience working as an ML engineer in industry
• Technical leadership experience, including leading 5-10 ICs on complex projects in industry
• 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 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
• 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 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
• Experience with data manipulation and processing, such as SQL, Cypher or Pandas
• 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