Machine Learning Scientist – Clinical Prediction

Iambic Therapeutics, Inc.

Job Overview

Location

Boston Office

Job Type

Full-time

Full Job Description

📋 Description

• Fine-tune large-scale multimodal transformer models for clinical and biomedical applications, specifically targeting patient- and trial-level outcome prediction, safety/toxicity modeling, and PK/PD response modeling.
• Identify, curate, and evaluate diverse datasets from biomedical sources to support clinical prediction tasks, ensuring data quality and relevance to drug discovery.
• Develop and apply rigorous experimental frameworks that prevent data leakage across multiple dimensions including temporal, trial-family, metadata, ontological, and arm-comparator sources.
• Design and maintain benchmarking and evaluation frameworks to track model performance across tasks and model iterations, using clinically meaningful metrics.
• Build models with calibrated outputs and uncertainty quantification to support clinical decision-making and regulatory relevance.
• Collaborate with machine learning and software engineering teams to deploy, operationalize, and scale models into production environments.
• Partner with clinical scientists and pharmacologists to align model development with real-world drug discovery and development needs.
• Communicate model results, insights, and technical findings to internal teams, external partners, and at scientific conferences.
• Generate high-quality, production-grade code: refactor, test, document, and package ML components to ensure reproducibility and team velocity.
• Optimize training workflows and orchestrate large-scale model runs using modern ML infrastructure including Docker, CUDA, and Kubernetes.
• Implement experiment tracking using tools such as Weights & Biases to maintain transparency and auditability across research workflows.
• Contribute to the end-to-end pipeline from data ingestion and preprocessing through model evaluation and deployment in a clinical drug discovery context.
• Ensure all modeling efforts are grounded in pharmacokinetic, pharmacodynamic, and adverse event data to drive therapeutic decision-making.
• Maintain strong engineering habits including reproducible experimentation, appropriate control strategies, clean code practices, and comprehensive testing.

🎯 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
• 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, and storytelling
• Enough independence to own a workstream from data ingestion through evaluation
• Strong engineering habits: reproducible experimentation, appropriate control strategy, clean code, testing

🏖️ Benefits

• Industry-leading competitive pay
• Company-paid healthcare
• Flexible spending accounts
• Voluntary life insurance
• 401K matching
• Uncapped vacation

Skills & Technologies

Python

Docker

Kubernetes

Onsite

Degree Required

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Iambic Therapeutics, Inc.

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About Iambic Therapeutics, Inc.

Iambic Therapeutics is a biotechnology company leveraging a cutting-edge, AI-driven platform to revolutionize drug discovery and develop superior medicines. Utilizing physics-based AI algorithms and high-throughput experimental processes, Iambic addresses challenging design problems to generate optimized drug candidates and explore novel mechanisms of action. Their platform-driven pipeline focuses on first-in-class and best-in-class programs, including a HER2 program already in Phase 1 clinical studies, aimed at unlocking the potential of known targets and transforming undruggable targets into breakthrough treatments for patients with unmet medical needs. This innovative approach enables them to deliver differentiated clinical candidates at an accelerated pace, supported by over $100 million raised in an oversubscribed financing round to advance their portfolio.

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