Machine Learning Scientist – Large Multimodal Models

. Research and implement architectural improvements to large-scale multimodal transformer models for biomedical applications .

Posted 5/7/2026full-timeBoston • Massachusetts • 🇺🇸 United StatesMid-LevelSenior💰 $148,000 - $210,000 per yearWebsite

Tools & technologies

DockerKubernetesPythonPyTorch

Key responsibilities & impact

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

What you’ll need

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)
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

Comp & perks

ATS Keywords

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Hard Skills & Tools

PythonPyTorchtransformer modelsdeep learningtraining optimizationinference optimizationmultimodal architecturesdistributed trainingHPCclean code

Soft Skills

communicationmentoringcollaborationtechnical guidancecode reviewsbest practicesresearchproblem-solvingteamworkstrategic planning