🛠 Tech Stack

💼 About This Role

You'll join the Knowledge Enrichment team at BenchSci to design and implement ML-based approaches for analyzing complex biomedical data. You'll build models that enrich a biological knowledge graph using graph ML and NLP. This role offers the chance to work with cutting-edge graph neural networks and RAG architectures.

🎯 What You'll Do

• Analyze and manipulate a large biological knowledge graph to identify enrichment opportunities.
• Design and develop ML approaches for classification, clustering, and implicit relationship discovery.
• Deliver robust, scalable, production-ready ML models with focus on performance.
• Architect ML solutions from data collection through deployment and monitoring.

📋 Requirements

• 3-5+ years experience as an ML engineer with agile delivery.
• Proficiency in Python and PyTorch with full ML lifecycle experience.
• Experience with graph machine learning (GNN, graph data science) and knowledge graphs.
• Familiarity with LLMs and RAG architectures (Graph RAG, Vector RAG).

✨ Nice to Have

• PhD in a relevant field (Software Engineering, Computer Science).
• Experience in the scientific/biological domain with ontologies.
• Technical leadership experience on complex projects.

🎁 Benefits & Perks

• 🏖️ Unlimited PTO
• 🏥 Comprehensive health benefits
• 📈 Equity options
• 💻 Hybrid work model (2 days/week in office)
• 🌱 Professional development budget