AI/ML Scientist – Protein Foundation Models
Boston, MA or San Francisco, CA
USD 120K-200K (estimate) Mid-level Full Time
Tasks
- Design and execute ML experiments with evaluation frameworks and analysis
- Develop and scale distributed multi GPU and multi node training pipelines
- Document training decisions and produce research analysis
- Establish best practices for mixed precision training and gradient checkpointing
- Integrate foundation model outputs into protein binder design
- Optimize training methodology architecture selection and training infrastructure
- Pretrain and fine tune protein foundation models
Perks/Benefits
Skills/Tech-stack
AWS | Alphafold | Attention Mechanisms | Diffusion Models | Distributed Computing | Distributed Training | Experimental Design | Flow matching | Gradient Checkpointing | JAX | Kubernetes | Mixed Precision | Mixed-precision training | MmCIF | Multi-GPU | Multi-node | Multi-node Training | NumPy | PDB | Pandas | Protein Sequence Data | Protein structure | Protein structure data | PyTorch | Python | Ray | Scikit-learn | Sequence data | Statistical Analysis | Structure data | Transformers
Education
Roles
AI | AI/ML Scientist | Learning Scientist | ML Scientist | Machine Learning Scientist | Scientist
Regions
Countries
States
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