Senior Machine Learning Engineer

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BenchSci

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We empower scientists with the world’s most advanced biomedical artificial intelligence.

Antibody • Research • Machine Learning • Online Platform • Science

201 - 500

💰 Series C on 2022-01

Description

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

Requirements

• 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 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 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. • 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. • Outstanding verbal and written communication skills. • A growth mindset continuously seeking to stay up-to-date with cutting-edge advances in ML/AI, complemented by actively engaging with the ML/AI community.