Join Iambic Therapeutics as a Software Engineer to develop agentic data pipelines for biomedical data using LLMs.
Posted by employer 1 month ago
First seen on Joblaze 1 month ago
Last verified on the company career page 6 days ago
AI in the day-to-day
Developing AI agents that can navigate heterogeneous data sources and enforce quality standards.
Requirements
Not disclosed in this posting: compensation, visa sponsorship.
Benefits
Joblaze summary
In this role, the software engineer focuses on designing and maintaining systems that automate the acquisition and processing of biomedical data for Iambic Therapeutics' Enchant model. Key skills include strong Python programming, experience with LLM APIs, and a solid understanding of data engineering principles, particularly in the biomedical domain. This position is well-suited for candidates with a background in computational STEM fields and a passion for leveraging AI to tackle complex data challenges. The team operates at the cutting edge of AI-driven drug discovery, emphasizing collaboration and innovation.
Joblaze insights
Quick facts
From the original posting
JOB SUMMARY
We are seeking a software engineer 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 generate code to acquire, clean, format, quality-control, and generate auditable data reports for the large-scale datasets that power Enchant training. The model writes the code, the code runs the pipeline. 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 that turn a pointer to a biomedical data source (web page, S3 path, GitHub repository, a table in a paper) into a reviewed, versioned dataset. The agent's output is committed pipeline code and its test suite, so re-running it later reproduces the same dataset. 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, tool definitions, validation loops, and evaluation harnesses that make agent-generated code trustworthy, improving reliability and throughput over time
Collaborate with ML scientists on the Enchant team to understand data requirements and translate them into scalable acquisition and processing systems
Monitor and maintain distributed data pipelines in production, diagnosing failures and improving robustness over time
Document data provenance, processing decisions, and quality metrics to support reproducibility and auditing
Operate the agents safely with sandboxed execution, least-privilege credentials, restricted network access, audit logs, and raising potential security risks to the team
QUALIFICATIONS
Master’s degree in a computational STEM field, or a Bachelor's with 2+ years of 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
Hands-on experience with Python testing frameworks (e.g., pytest fixtures, parametrization)
PREFERRED QUALIFICATIONS
Experience with agent orchestration frameworks, and with evaluation harnesses for LLM-generated code
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
Knowledge of agent security practices: sandboxing, scoped credentials, prompt injection
Interest in a longer-term project: a natural language orchestrator that lets drug prosecution team members request inference, fine-tuning, virtual screens, and dataset analysis without writing code using our internal tools
ABOUT IAMBIC THERAPEUTICS
MISSION & CORE VALUES
PAY AND BENEFITS
Standard company text repeated across Iambic Therapeutics's postings is omitted here.
Explore more