Machine Learning Scientist — Large multimodal models

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Relocation assistance

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Learning budget

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Salary

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Company stage

Growth-stage From the posting source checked Jun 20, 2026

Application

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Assessment

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Deadline

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Where they hire

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About this role

Machine Learning Scientist — Large multimodal models Boston Office, Boston, MA, US 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

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