Machine Learning Scientist — Large multimodal models

Iambic Therapeutics, Inc•Boston, MA

1d•Hybrid

About The Position

We are seeking a Machine Learning Scientist to join the Enchant team at Iambic Therapeutics. Our mission is to deliver better medicines through innovation in AI-based discovery technologies. In this role, you will research, develop, and scale Enchant — our multimodal transformer model trained on a wide variety of biomedical data — pushing the boundaries of what large-scale foundation models can achieve in drug discovery. This role spans architecture research through to production deployment. You will design and evaluate new model architectures, develop hybrid modeling approaches, optimize training and inference at scale, and work with colleagues across ML and drug discovery to put these models into the hands of scientists making real therapeutic decisions. We are hiring flexibly across levels, from Associate Scientist through Research Scientist II, depending on experience.

Requirements

MS in ML/CS or a computational STEM field with relevant industry or research experience, or PhD or equivalent industry experience demonstrating comparable depth
Strong Python and PyTorch experience, including implementing and training deep learning models end-to-end
Demonstrated experience training transformer models at scale
Strong engineering habits: reproducible experimentation, clean code, testing, and performance-minded debugging
Comfort working with modern ML infrastructure (e.g., Docker, CUDA, Kubernetes, experiment tracking such as Weights & Biases)

Nice To Haves

Experience with multimodal or multi-task model architectures
Training and inference optimization (e.g., mixed precision, kernel optimization, quantization, distributed strategies)
Familiarity with biomedical, chemical, or biological data domains
Distributed training at scale
HPC or large-scale training operations experience

Responsibilities

Research and implement architectural improvements to large-scale multimodal transformer models for biomedical applications
Investigate hybrid modeling approaches that combine learned representations with domain-informed structure or inductive biases
Optimize training pipelines for efficiency, stability, and scalability across many-GPU clusters
Develop and apply inference optimization techniques to support deployment in interactive discovery workflows
Design and maintain benchmarking and evaluation frameworks that track model quality across modalities and downstream tasks
Collaborate with ML and software engineering colleagues to deploy and operationalize models
Partner with computational chemists, medicinal chemists, and biologists to ensure model development is grounded in drug discovery needs
Communicate results to internal teams, external partners, and at conferences
Write high-quality research and engineering code: refactor, test, document, and package ML components to support team velocity
Mentor interns and junior team members through technical guidance, code reviews, and best practices in ML experimentation
Contribute to the strategic research roadmap for Enchant and related multimodal technologies