AI/ML Scientist, Protein Foundation Models

Responsibilities

• Advance the team's ongoing foundation model training efforts—pretraining, fine-tuning, and evaluating folding, docking, language, and generative design models on Manifold's proprietary experimental data
• Bring depth in training methodology, architecture selection, and optimization to complement the existing team's expertise
• Develop and scale training pipelines for distributed, multi-GPU and multi-node training runs
• Integrate foundation model outputs into mBER to improve binder design success rates and enable new design capabilities
• Design and execute ML experiments with clear hypotheses, rigorous evaluation frameworks, and systematic analysis
• Establish best practices for mixed-precision training, gradient checkpointing, and computational efficiency at scale
• Produce clear documentation and analysis supporting architecture and training decisions

Required Qualifications

• Demonstrated experience pretraining and/or fine-tuning protein foundation models (folding, docking, language models, or generative design) with published or otherwise demonstrable results
• Strong familiarity with AlphaFold architecture and training methodology
• 2+ years of hands-on experience with PyTorch and/or JAX for deep learning
• Experience with large-scale model training: distributed training, multi-GPU/multi-node setups, mixed precision, gradient checkpointing
• Solid understanding of deep learning architectures (transformers, attention mechanisms, diffusion/flow matching) and optimization techniques
• Experience working with protein structure data (PDB, mmCIF) and/or protein sequence datasets
• Strong statistical analysis and experimental design skills
• Proficiency in Python scientific computing stack (NumPy, Pandas, scikit-learn)
• Self-directed researcher who can balance guidance with independence
• Excellent written and verbal communication skills for cross-functional collaboration

Preferred Qualifications

• Experience with protein generative design methods (e.g., RFdiffusion, ProteinMPNN, flow matching approaches)
• Experience with protein language models (e.g., ESM family)
• Published research in computational biology, protein design, or structural biology
• Experience training on proprietary or domain-specific biological datasets
• Familiarity with Ray for distributed computing
• Experience with Kubernetes (EKS) and cloud computing platforms (AWS)
• Knowledge of protein engineering, directed evolution, or structural biology wet lab techniques
• Experience working with agentic AI coding tools for fast, parallelized execution of modeling experiments
• Previous biotech/pharma industry experience