About the job
As part of our team, you will help to accelerate and optimize our progress in agentic AI methods for biological and clinical data curation and analysis. In this role, you will be an integral part of our multidisciplinary teams building the computational platforms that will enable Altos to achieve its mission. You will collaborate with biomedical research experts as well as other machine learning scientists and engineers across the Institute of Computation to contribute to the Altos research and translation ecosystem, focusing on designing and building state-of-the-art agentic AI systems and workflows that tackle biological questions, accelerate clinical data analysis, and aid in the discovery of novel interventions for aging and disease.
Responsibilities
Design and develop agentic AI workflows for biological and clinical data curation tasks
Research and implement advanced context engineering techniques (RAG, agentic RAG, graph RAG) tailored to biomedical data
Develop and optimize prompts and agent architectures for reliability, accuracy, and scientific rigor
Build LLM-based tool-use systems and integrations relevant to bioinformatics and clinical R&D pipelines
Collaborate with bioinformatics scientists, clinical researchers, and domain experts to identify automation opportunities and translate them into agentic solutions
Evaluate and benchmark agentic systems, establishing rigorous metrics for performance and correctness in scientific contexts
Stay current with the rapidly evolving landscape of agentic AI methods and contribute to internal best practices
Qualifications
Minimum
PhD in Computer Science, Machine Learning, Bioinformatics, or a related field
Demonstrated experience developing agentic AI systems, including LLM-based tool use, prompt engineering, and context engineering techniques
Strong foundation in machine learning principles and their application to real-world problems
Strong background in bioinformatics, computational biology, or clinical R&D
Familiarity with MCP server development and agent skill creation
Very strong programming skills in Python, with experience writing production-quality, well-documented code
Strong track record of published peer-reviewed research in AI/ML and/or computational biology
Preferred
Experience with AWS services (S3, Bedrock, Lambda) in the context of AI/ML workflows
Experience with clinical trial data, regulatory data curation, or biomedical ontologies (e.g., CDISC, SNOMED, MedDRA)
Track record working with NGS data (e.g., RNA-seq, ATAC-seq, DNA methylation) or other biological data modalities
Experience designing evaluation frameworks and benchmarks for agentic AI systems
Familiarity with knowledge graph construction and graph-based retrieval methods