Machine Learning Scientist — Large multimodal models

Iambic; Boston (MA); Hybrid; USD 148,000 - 210,000; Full time

Benefits offered by this job

Company-paid healthcare

Flexible spending accounts

401K matching

Uncapped vacation

Job summary

Iambic is looking for a Machine Learning Scientist to join the Enchant team. You will work on optimizing large-scale transformer models for drug discovery, develop innovative modeling techniques, and collaborate with interdisciplinary teams. Required qualifications include an MS or PhD in a relevant field and strong experience with Python and PyTorch. The position offers a competitive salary range of $148K - $210K, with remote work options in the US or UK and on-site opportunities in Boston.

Qualifications

• Master's degree in ML/CS or related field with relevant industry experience.
• Strong experience in Python and PyTorch for deep learning models.

Responsibilities

• Research and implement architectural improvements for transformer models.
• Optimize training pipelines for efficiency and scalability.
• Collaborate with colleagues to deploy and operationalize models.

Skills

Python

PyTorch

Transformer models

Docker

Kubernetes

Experiment tracking

Education

MS in ML/CS or a computational STEM field

PhD or equivalent industry experience

Job description

Job Summary

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.

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.
• 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.

Location

Remote (US or UK). On‑site available in Bristol, UK and Boston, US.

Pay And Benefits

We offer industry‑leading competitive pay, company‑paid healthcare, flexible spending accounts, voluntary life insurance, 401K matching, and uncapped vacation to our team. Compensation Range: $148K - $210K.