BenchSci is hiring a
Lead Machine Learning Engineer, Remote - United Kingdom
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Lead Machine Learning Engineer
🏢 BenchSci
💵 $150k-$250k
📍United Kingdom
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Summary
BenchSci is seeking a Lead Machine Learning Engineer to join their Knowledge Enrichment team. The role involves designing and implementing ML-based approaches for analyzing complex biomedical data, collaborating with team members on applying state-of-the-art ML algorithms, and delivering robust, scalable, and production-ready ML models.
Requirements
- Minimum 5, ideally 8+ years of experience working as an ML engineer
- Minimum 1, ideally 3+ years technical leadership experience, including leading 5-10 ICs technically 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 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
- Comprehensive knowledge of software engineering, programming fundamentals and industry experience using Python
- Experience with data manipulation and processing, such as SQL, Cypher or Pandas
Responsibilities
- Design and implement ML-based approaches to analyze complex biomedical data
- Collaborate with team members in applying state of the art ML and graph ML/data science algorithms to this data
- 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 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
Preferred Qualifications
- 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
- 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!
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