<p>What You Will Contribute To Altos</p> <p>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.</p> <p>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.</p> <p>The successful candidate will combine deep expertise in agentic AI methods with a strong foundation in bioinformatics and/or clinical R&D. You will work closely with domain experts to translate complex biological and clinical data challenges into intelligent, automated solutions. You will thrive in a fast-paced environment that stresses teamwork, transparency, scientific excellence, originality, and integrity.</p> <p>Responsibilities</p> <ul> <li>Design and develop agentic AI workflows for biological and clinical data curation tasks </li><li>Research and implement advanced context engineering techniques (RAG, agentic RAG, graph RAG) tailored to biomedical data </li><li>Develop and optimize prompts and agent architectures for reliability, accuracy, and scientific rigor </li><li>Build LLM-based tool-use systems and integrations relevant to bioinformatics and clinical R&D pipelines </li><li>Collaborate with bioinformatics scientists, clinical researchers, and domain experts to identify automation opportunities and translate them into agentic solutions </li><li>Evaluate and benchmark agentic systems, establishing rigorous metrics for performance and correctness in scientific contexts </li><li>Stay current with the rapidly evolving landscape of agentic AI methods and contribute to internal best practices </li></ul> <p>Who You Are</p> <ul> <li>Proven track record leveraging machine learning and AI to solve real-world problems </li><li>Expertise in agentic AI methods development, including prompt optimization, LLM-based tool use, context engineering (RAG, agentic RAG, graph RAG), and agent evaluation </li><li>Experience writing production-quality code with agentic frameworks (e.g., LangGraph, Pydantic-AI, DSPy, or similar) </li><li>Deep familiarity with bioinformatics and/or clinical R&D, with the ability to engage meaningfully with domain experts on biological and clinical questions </li><li>Proficiency with AI-assisted development tools (e.g., Claude Code, Cursor) to iterate rapidly </li><li>A team player who thrives in collaborative environments and is committed to enabling colleagues to reach their full potential through giving and requesting feedback focused on professional growth </li><li>Able to advise others across the wider function / company on cutting edge agentic AI practices and approaches to enable the science / research. Desire to constantly expand your skillset and knowledge. Keen to learn more about biology, machine learning, and medicine </li><li>Inspired by the Altos mission of restoring cell health and resilience to reverse disease, injury, and age-related disabilities </li></ul> <p>Minimum Qualifications</p> <ul> <li>PhD in Computer Science, Machine Learning, Bioinformatics, or a related field </li><li>Demonstrated experience developing agentic AI systems, including LLM-based tool use, prompt engineering, and context engineering techniques </li><li>Strong foundation in machine learning principles and their application to real-world problems </li><li>Strong background in bioinformatics, computational biology, or clinical R&D </li><li>Familiarity with MCP server development and agent skill creation </li><li>Very strong programming skills in Python, with experience writing production-quality, well-documented code </li><li>Strong track record of published peer-reviewed research in AI/ML and/or computational biology </li></ul> <p>Preferred Qualifications</p> <ul> <li>Experience with AWS services (S3, Bedrock, Lambda) in the context of AI/ML workflows </li><li>Experience with clinical trial data, regulatory data curation, or biomedical ontologies (e.g., CDISC, SNOMED, MedDRA) </li><li>Track record working with NGS data (e.g., RNA-seq, ATAC-seq, DNA methylation) or other biological data modalities </li><li>Experience designing evaluation frameworks and benchmarks for agentic AI systems </li><li>Familiarity with knowledge graph construction and graph-based retrieval methods </li></ul> <p>The salary range for Redwood City:</p> <ul> <li>Senior Scientist I, Machine Learning: $257,400 - $330,000 </li></ul> <p>The salary range for San Diego:</p> <ul> <li>Senior Scientist I, Machine Learning: $239,500 - $307,000 </li></ul> <p>Exact compensation may vary based on skills, experience, and location.</p> <p>#LI-NN1</p> <p>For UK applicants, before submitting your application:</p> <ul> <li>Please click here to read the Altos Labs EU and UK Applicant Privacy Notice (bit.ly/eu_uk_privacy_notice) </li><li>This Privacy Notice is not a contract, express or implied and it does not set terms or conditions of employment. </li></ul>