Senior Machine Learning Engineer - Graph ML

Team: Machine Learning

Location: Toronto, Ontario, London, England

Commitment: Full-Time (Hybrid 2 Days/Week)

Workplace Type: hybrid

You Will:

• Analize and manipulate a large, highly connected biological knowledge graph constructed of data from multiple heterogeneous sources, 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 optimizing 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, and 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.

You Have:

• 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 utilize 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 optimizing 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, and a deep understanding of how to implement solutions using Retrieval Augmented Generation (RAG) architectures, including both Graph RAG and Vector RAG.
• Experience with graph machine learning (i.e. graph neural networks, graph data science) and practical applications thereof. Your experience working with Knowledge Graphs, ideally biological, and a familiarity with biological ontologies complement this.
• 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, like 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 if this environment suits you.
• You have experience working in cross-functional teams with product managers, scientists, project managers, and 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, complemented by actively engaging with the ML/AI community.