🛠 Tech Stack

💼 About This Role

You'll lead the delivery of federated co-folding models for drug discovery networks used by top pharma partners. Your core impact is translating complex scientific goals into production-ready AI pipelines. This role bridges research and engineering at scale in a remote-first environment.

🎯 What You'll Do

• Lead end-to-end delivery of federated structural biology model systems
• Design, fine-tune, and extend large-scale foundation models for structural biology
• Translate scientific objectives into clear execution plans and milestones
• Define model evaluation criteria and ensure high-quality validated results

📋 Requirements

• PhD, MSc, or equivalent in ML, Computational Biology, or related field with 5+ years applied ML experience
• Strong hands-on expertise in structural biology ML including protein modeling or co-folding
• Experience with modern ML frameworks like Python and PyTorch
• Experience with MLOps or ML infrastructure including Kubernetes-based pipelines

✨ Nice to Have

• Experience in federated learning or distributed training
• Publication record in top-tier ML venues (NeurIPS, ICML, ICLR)
• Experience in regulated environments (pharma, biotech)

🎁 Benefits & Perks

• 💰 Competitive compensation including virtual equity options
• 🏠 Remote-first working model with flexibility
• 🏖️ Generous annual holiday allowance
• 📚 Learning budget and wellbeing support
• 🚀 Cutting-edge AI systems applied to drug discovery

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Phone Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Team Fit Interview· 45 min