Lead Machine Learning Engineer - Knowledge Enrichment

November 12

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BenchSci

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Antibody • Research • Machine Learning • Online Platform • Science

201 - 500

💰 Series C on 2022-01

Description

• We are looking for a Senior Machine Learning Engineer to join our new Knowledge Enrichment team at BenchSci. • You will help design and implement ML-based approaches to analyse, extract and generate knowledge from complex biomedical data such as experimental protocols and from results from several heterogeneous sources, including both publicly available data and proprietary internal data, represented in unstructured text and knowledge graphs. • The data will be leveraged in order to enrich BenchSci’s knowledge graph through classification, discovery of high value implicit relationships, predicting novel insights/hypotheses, and other ML techniques. You will collaborate with your team members in applying state of the art ML and graph ML/data science algorithms to this data. • You are comfortable working in a team that pushes the boundaries of what is possible with cutting edge ML/AI, challenges the status quo, is laser focused on value delivery in a fail-fast environment.

Requirements

• 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