Machine Learning Scientist — Agentic data pipelines
Iambic Therapeutics - Boston Office, Boston, MA, US
Posted May 6, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
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- Mental health support
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- Relocation assistance
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Schedule
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- Weekend work
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Application
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About this role
Machine Learning Scientist — Agentic data pipelines Boston Office, Boston, MA, US Job Summary We are seeking a scientist to join our team at Iambic Therapeutics, working on data acquisition and curation for Enchant , our multimodal transformer model trained at scale on a wide variety of biomedical data. In this role, you will design and build agentic systems that acquire, clean, format, and quality-control the large-scale datasets that power Enchant training. You will work at the intersection of LLM-based automation and biomedical data engineering-developing AI agents that can navigate heterogeneous data sources, enforce quality standards, and operate reliably at scale. This role is ideal for candidates who combine strong software engineering instincts with scientific understanding of biomedical data, and who are excited about using LLMs as tools to solve practical data problems. Key Responsibilities - Design, build, and maintain agentic systems for automated data acquisition from public and proprietary biomedical data sources - Develop LLM-based pipelines for data cleaning, normalization, and formatting across diverse data modalities (e.g., molecular, genomic, clinical, literature) - Implement automated quality-control workflows that detect anomalies, flag inconsistencies, and enforce data standards - Evaluate and iterate on agent architectures, prompting strategies, and tool-use patterns to improve reliability and throughput - Collaborate with ML scientists on the Enchant team to understand data requirements and translate them into scalable acquisition and processing systems - Monitor and maintain data pipelines in production, diagnosing failures and improving robustness over time - Document data provenance, processing decisions, and quality metrics to
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