Machine Learning Intern
Core
Build, optimize, and scale real-world applications of bio foundation models, translating research into production-grade systems.
Role type
Machine Learning Engineer (Intern)
Builds
Scalable training/inference pipelines for biological foundation models (Transformers, SSMs, Diffusion Models)
Domain
Biotechnology / Pharma / AI Infrastructure
Deliverable
production ML models
Required skills
Python, PyTorch/JAX/TensorFlow, ML pipeline scaling, MLOps (Weights & Biases, Ray, Docker), modern ML architectures (Transformers, Diffusion Models, SSMs), distributed compute, experiment tracking
Preferred skills
Open-source contributions, model compression, foundation model benchmarking, API integration, biology/pharma domain knowledge
Technologies
PyTorch, JAX, TensorFlow, Weights & Biases, Ray, Docker, Transformers, SSMs, Diffusion Models
Responsibilities
Build and maintain scalable training/inference pipelines; Optimize model performance, latency, and throughput; Apply post-training research to architectures; Design modular ML components; Collaborate to turn notebooks into production systems; Own ML infrastructure components; Drive experimentation on fine-tuning and transfer learning
Seniority
Intern
