ML Scientist, Biomolecular Foundation Models

What You'll Do

• Spearhead the design and development of cutting-edge deep learning models aimed at advancing generative biomolecular design.
• Design novel representation and tokenization of biomolecules to enable more efficient transfer learning.
• Own and establish a robust research infrastructure.

What You'll Need

• MSc or PhD in Computer Science, AI/ML, Physics, Biophysics, or a related field.
• 2+ years of internship/full-time industry or postdoctoral ML research experience.
• 2+ years of experience developing production-grade machine learning solutions.
• Experience training large-scale generative (transformer/diffusion) models on HPC or multi-GPU, multi-node distributed systems.
• Proficiency in Python and relevant ML libraries (PyTorch or TensorFlow).
• Proven ability to lead technical initiatives, communicate progress, and drive innovation in generative biomolecular design.
• **(Preferred)** Contributed to open-source data projects in biology/chemistry.
• **(Preferred)** Strong presence in the ML community through publications and conference proceedings.
• **(Preferred)** Knowledge of biological data formats (FASTA, SDF, PDBx/mmCIF) and domain-specific ontologies/metadata standards.
• **(Preferred)** Experience with models like ESM, DiffDock, ProteinMPNN, OpenFold, etc.
• **(Preferred)** Familiarity with ML software development and cloud services (Docker, AWS Batch, Step Functions, EKS, AWS ParallelCluster, etc.).
• **(Preferred)** Experience with libraries like: rdkit, rosetta, prody, biopython, pymol, openmm, dagster, prefect, etc.