Machine Learning Scientist at Iambic Therapeutics in San Diego, CA

Summary: Iambic Therapeutics is a clinical-stage life-science and technology company developing novel medicines using its AI-driven discovery platform. They are seeking a Machine Learning Scientist to work on transformer-based diffusion methods for predicting biomolecular structures, contributing to advancements in drug discovery. Responsibilities: - Design, implement, and train discrete and continuous diffusion models for predicting biomolecular structure tokens - Develop and iterate on structure tokenizers, including vector-quantized representations of 3D molecular and protein structure - Build and maintain data processing pipelines for large-scale biomolecular structure datasets - Train models on multi-GPU clusters, managing large-scale training runs - Develop rigorous benchmarking and evaluation workflows; validate against external benchmarks while prioritizing internal discovery-relevant metrics - Collaborate with ML scientists, computational chemists, and drug discovery teams to integrate models into discovery workflows - Communicate results to internal teams, external partners, and at scientific conferences - Mentor interns and junior team members through code reviews, technical guidance, and best practices (Senior level) Required Qualifications: - PhD in machine learning, computer science, computational chemistry, physics, or a related computational STEM field, or equivalent industry experience demonstrating comparable depth - Strong Python and PyTorch skills, including end-to-end implementation and training of deep learning models - Demonstrated experience in one or more of the following: 3D atomistic or molecular modeling, Vector quantization and learned discrete representations, Diffusion, flow-matching, or related generative modeling in continuous vector spaces - Strong engineering practices: reproducible experimentation, clean code, testing, and performance-aware debugging - Comfort with modern ML infrastructure (e.g., Docker, CUDA, Kubernetes, experiment tracking tools such as Weights & Biases) Preferred Qualifications: - Experience with discrete diffusion, masked generative or transfusion models - Protein–ligand modeling, structure prediction, or structure-based drug discovery - Geometric deep learning and/or equivariant architectures - Multi-GPU / distributed training at scale - Docking, molecular dynamics, or other biomolecular simulation methods - RS II and Senior candidates are expected to bring industry experience applying ML methods to drug discovery or related scientific domains - Senior candidates are expected to have a track record of independently driving research direction and mentoring others Required Skills: Machine Learning, Python, PyTorch, 3D Molecular Modeling, Vector Quantization, Generative Modeling, Diffusion Models, Reproducible Experimentation, Clean Code, Testing, Performance-aware Debugging, Docker, CUDA, Kubernetes, Experiment Tracking Benefits: Company paid healthcare, Flexible spending accounts, Voluntary life insurance, 401K matching, Uncapped vacation