Machine Learning Scientist – Clinical Prediction

Iambic Therapeutics, Inc•Boston, MA

10d•$148,000 - $186,000•Onsite

About The Position

We are seeking a Machine Learning Scientist to join the Enchant team at Iambic Therapeutics. In this role, you will design and implement clinical fine-tuning of 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 data sourcing through to production deployment. You will identify, curate, and evaluate datasets that support prediction of relevant clinical endpoints (patient- and trial-level outcome modeling, safety/toxicity prediction, and PK/PD response modeling) and fine-tune Enchant to deliver critical clinical insights. This includes developing rigorous, leakage-resistant experimental frameworks, optimizing training, orchestrating runs at scale, and working with colleagues across ML and clinical functions to put these models into the hands of scientists making real therapeutic decisions.

Requirements

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)

Nice To Haves

Experience building and deploying clinically relevant prediction models
Familiarity with ClinicalTrials.gov/AACT data
Experience with MedDRA, pharmacovigilance, or adverse event data
Direct exposure to multi-task learning
Hands-on experience with agentic data extraction
HPC or large-scale computing experience

Responsibilities

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