Senior Machine Learning Engineer - Knowledge Graph(Remote)

BenchSci; London; Remote; GBP 70,000 - 100,000; Full time

Job summary

BenchSci is seeking a Senior Machine Learning Engineer to join their Knowledge Enrichment team in London. This role focuses on leveraging state-of-the-art ML techniques to enrich biomedical knowledge graphs, collaborating with experts in the field to enhance drug discovery efforts. Candidates should have a strong background in ML, experience with Python and PyTorch, and an advanced degree in a relevant field.

Qualifications

• Minimum 5+ years of experience as an ML engineer.
• Experience in leading complex projects.
• Familiar with biological ontologies and knowledge graphs.

Responsibilities

• Design and implement ML-based approaches for biomedical data.
• Deliver robust, scalable ML models and solutions.
• Collaborate with cross-functional teams including product managers and engineers.

Skills

Machine Learning Proficiency

Python

PyTorch

NLP Techniques

Problem Solving

Communication Skills

Education

PhD in Software Engineering or Computer Science

Tools

SQL

Pandas

Job description

We are looking for a Senior Machine Learning Engineer to join our Knowledge Enrichment team at BenchSci.

You will help design and implement ML-based approaches to analyze, extract, and generate knowledge from complex biomedical data, including experimental protocols and results from heterogeneous sources such as publicly available and proprietary internal data, represented in unstructured text and knowledge graphs. You will work alongside some of the brightest minds in tech, leveraging state-of-the-art approaches to deliver on BenchSci’s mission to expedite drug discovery. Knowledge Enrichment is central to this challenge, ensuring we can reason over and gain insights from an extensive, accurate, and high-quality representation of biomedical data.

The data will be used to enrich BenchSci’s knowledge graph through classification, discovery of implicit relationships, prediction of novel insights/hypotheses, and other ML techniques. You will collaborate with team members to apply advanced 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, and is focused on value delivery in a fail-fast environment.

You Will:

• Analyze and manipulate a large, highly-connected biological knowledge graph from multiple sources to identify enrichment opportunities
• Work with data and knowledge engineering experts to develop knowledge enrichment strategies
• Provide solutions for classification, clustering, querying, discovering relationships, and inference to reveal insights
• Deliver robust, scalable, and production-ready ML models, optimizing performance and efficiency
• Design ML solutions from data collection to deployment and monitoring
• Collaborate across functions including product management, science, and engineering
• Provide technical leadership on Knowledge Enrichment projects using ML
• Work closely with other ML engineers to ensure alignment
• Engage with stakeholders from different functions
• Promote ML best practices and participate in agile practices

You Have:

• Minimum 3, preferably 5+ years of experience as an ML engineer
• Some experience providing technical leadership
• Degree, preferably PhD, in Software Engineering, Computer Science, or similar
• Proven track record of delivering complex ML projects in an agile environment
• Deep understanding of NLP and ML techniques
• Mastery of ML frameworks and libraries, capable of architecting complex systems
• Extensive experience with Python and PyTorch
• Experience with the full ML lifecycle from design to deployment
• Knowledge of Large Language Models and Retrieval Augmented Generation (RAG)
• Experience with graph machine learning and biological knowledge graphs
• Familiarity with biological ontologies
• Strong problem-solving skills with attention to scalability and performance
• Proficiency in software engineering fundamentals and Python programming
• Experience with data manipulation tools like SQL, Cypher, or Pandas
• Proactive attitude with a sense of ownership
• Experience working in cross-functional teams, ideally in the scientific/biological domain
• Excellent communication skills to explain complex concepts to diverse audiences
• Growth mindset and active engagement with the ML/AI community