Machine Learning Scientist — Large multimodal models

Job Summary

We are seeking a Machine Learning Scientist to join the [_Enchant_](https://www.iambic.ai/post/enchantv2) 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.

Key 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

Depending on level:

• 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

Qualifications

Required:

• 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)

Desired:

• 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