Senior Machine Learning Engineer (Remote)

March 2

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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 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, including data collection and preparation, model selection, training, fine-tuning and evaluation, to deployment and maintenance. • 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

Requirements

• 5+ years of experience working as an ML engineer. • 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. • Ideally you have held technical leadership roles/responsibilities. • Demonstrable ML proficiency with a deep understanding of how to utilise state of the art ML techniques, specifically related to NLP. • Mastery of several ML frameworks and libraries, with the ability to architect complex ML systems from scratch. • Extensive experience with NLP 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. • 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.

Benefits

• An engaging remote-first culture • A great compensation package that includes BenchSci equity options • A robust vacation policy plus an additional vacation day every year • Company closures for 14 more days throughout the year • Flex time for sick days, personal days, and religious holidays • Comprehensive health and dental benefits • Annual learning & development budget • A one-time home office set-up budget to use upon joining BenchSci • An annual lifestyle spending account allowance • Generous parental leave benefits with a top-up plan or paid time off options • The ability to save for your retirement coupled with a company match!