Machine Learning Scientist – Clinical Prediction

Iambic Therapeutics

Machine Learning Scientist focusing on clinical predictions using multimodal transformer models at Iambic Therapeutics. Responsible for data handling, model tuning, and collaboration with scientists.

Posted 6/18/2026full-timeBoston • Massachusetts • 🇺🇸 United StatesMid-LevelSenior💰 $148,000 - $186,000 per yearWebsite

Tech Stack

Tools & technologies

DockerKubernetesPython

About the role

Key responsibilities & impact

Fine-tune large-scale multimodal transformer models for clinical and biomedical applications
Identify, characterize, and utilize datasets that can deliver insights into pharmacokinetics (PK), pharmacodynamics (PD), toxicity, clinical adverse events, and clinical trial outcomes
Develop and apply rigorous experimental approaches that account for multiple sources of potential leakage (split, metadata, trial-family, temporal, ontological, arm-comparator, etc.)
Design and maintain benchmarking and evaluation frameworks that track model quality across models and tasks
Build models with appropriate calibration, uncertainty quantification, and clinically meaningful evaluation metrics
Collaborate with ML and software engineering colleagues to deploy and operationalize models
Partner with clinical scientists and pharmacologists to ensure model development is grounded in drug discovery and development needs
Communicate results to internal teams, external partners, and at conferences
Generate high-quality research and engineering code: refactor, test, document, and package ML components to support team velocity

Requirements

What you’ll need

MS in chemistry, bio/chemical engineering, or a computational STEM field with 3+ years of relevant industry or research experience, or PhD or equivalent industry experience demonstrating comparable depth
Strong Python experience, including implementing and fine-tuning deep learning models
Demonstrated experience in clinical science or working with clinical datasets
Excellent Data Science skills (problem framing, data sourcing, extraction, cleaning, visualization, EDA, modeling, tuning, storytelling, etc.)
Enough independence to own a workstream from data ingestion through evaluation
Strong engineering habits: reproducible experimentation, appropriate control strategy, clean code, testing
Comfort working with modern ML infrastructure (e.g., Docker, CUDA, Kubernetes, experiment tracking such as Weights & Biases)

Benefits

Comp & perks

company paid healthcare
flexible spending accounts
voluntary life insurance
401K matching
uncapped vacation
state-of-the art facility with an onsite gym and dining

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

multimodal transformer modelspharmacokineticspharmacodynamicstoxicologyclinical trial outcomesexperimental approachesmodel calibrationuncertainty quantificationdata sciencedeep learning

Soft Skills

collaborationcommunicationindependenceproblem framingstorytellingclean codetestingorganizationdocumentationrefactoring

Certifications

MS in chemistryMS in bio/chemical engineeringPhD in computational STEM field