Machine Learning Scientist — Agentic Data Pipelines

Iambic Iambic · Pharma · Boston, MA · Technology

Seeking a scientist to design and build agentic systems for automated data acquisition, cleaning, formatting, and quality-control of large-scale biomedical datasets to power a multimodal transformer model. This role involves developing LLM-based pipelines and automated quality-control workflows, evaluating agent architectures, and collaborating with ML scientists.

What you'd actually do

  1. Design, build, and maintain agentic systems for automated data acquisition from public and proprietary biomedical data sources
  2. Develop LLM-based pipelines for data cleaning, normalization, and formatting across diverse data modalities (e.g., molecular, genomic, clinical, literature)
  3. Implement automated quality-control workflows that detect anomalies, flag inconsistencies, and enforce data standards
  4. Evaluate and iterate on agent architectures, prompting strategies, and tool-use patterns to improve reliability and throughput
  5. Collaborate with ML scientists on the Enchant team to understand data requirements and translate them into scalable acquisition and processing systems

Skills

Required

  • Master's or PhD in a computational STEM field, or equivalent industry experience
  • Strong Python engineering skills, including experience building and maintaining production-quality software
  • Hands-on experience with LLM APIs (e.g., Claude, GPT) and agentic patterns such as tool use, orchestration, and multi-step reasoning
  • Familiarity with biomedical or chemical data sources and formats (e.g., PDB, UniProt, ChEMBL, SDF/MOL, FASTA, or similar)
  • Comfort with data engineering fundamentals: ETL design, data validation, and working with structured and unstructured data at scale

Nice to have

  • Experience with agent orchestration frameworks
  • Familiarity with cloud infrastructure and workflow orchestration (e.g., AWS, Docker, Kubernetes)
  • Knowledge of multimodal biomedical data—spanning small molecules, proteins, assays, images, ‘omics, and/or clinical records
  • Experience with large-scale dataset construction or curation for ML model training

What the JD emphasized

  • agentic systems
  • LLM-based pipelines
  • automated quality-control workflows
  • agent architectures
  • tool use
  • orchestration
  • multi-step reasoning
  • biomedical data
  • large-scale dataset construction or curation

Other signals

  • design and build agentic systems
  • LLM-based pipelines
  • automated quality-control workflows
  • evaluate and iterate on agent architectures