Senior Machine Learning Engineer - Graph ML
Description For Senior Machine Learning Engineer - Graph ML
BenchSci is revolutionizing life-saving research through their advanced biomedical AI platform. They're seeking a Senior Machine Learning Engineer to join their Knowledge Enrichment team, focusing on analyzing and extracting insights from complex biomedical data. The role combines cutting-edge ML/AI technology with practical applications in drug discovery, working with knowledge graphs and biological data. Backed by major investors including Google's AI fund, BenchSci serves 16 top pharmaceutical companies and 4,300+ academic centers. The position offers the opportunity to work with bright minds in tech, using state-of-the-art approaches in a remote environment. The company emphasizes a strong culture of transparency, continuous learning, and diversity, making it a certified Great Place to Work®. This role is perfect for someone passionate about applying ML to meaningful scientific challenges, comfortable with both technical leadership and hands-on development, and excited about working in a fast-paced, innovative environment that directly impacts scientific research.
Responsibilities For Senior Machine Learning Engineer - Graph ML
- Analyze and manipulate biological knowledge graph data from multiple sources
- Design and develop knowledge enrichment approaches
- Provide solutions for classification, clustering, and relationship discovery
- Deliver robust, scalable production-ready ML models
- Architect and design ML solutions from data collection to deployment
- Collaborate with cross-functional teams
- Provide technical leadership on Knowledge Enrichment projects
- Ensure adoption of ML best practices
Requirements For Senior Machine Learning Engineer - Graph ML
- Minimum 3, ideally 5+ years of experience as an ML engineer
- Degree, preferably PhD, in Software Engineering, Computer Science, or similar
- Experience with Python and PyTorch
- Expertise in NLP and ML techniques
- Experience with graph machine learning and neural networks
- Knowledge of biological ontologies
- Experience with data manipulation (SQL, Cypher, Pandas)
- Strong communication skills
- Experience with Large Language Models and RAG architecture
- Track record of delivering complex ML projects
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