AI Software Engineer (KBase Project)

Lawrence Berkeley National Laboratory•Berkeley, CA

5h•$117,132 - $146,400•Onsite

About The Position

Berkeley Lab’s (LBNL) Environmental Genomics and Systems Biology (EGSB) Division has an opening for an AI Software Engineer to join the US Department of Energy’s (DOE) Systems Biology Knowledgebase (KBase) team! In this exciting role, you will focus on developing software infrastructure for AI-driven scientific workflows, including intelligent agents that interact with KBase’s Data Lakehouse and biological knowledge resources. You will have the opportunity to work with KBase’s Principal Investigator (PI) and domain scientists, to develop AI agents, co-scientist tools, data services, knowledge graphs, and knowledge representations that enable AI to reason over harmonized biological data. The position emphasizes scalable infrastructure, scientifically rigorous AI-generated insights, and close collaboration with researchers. You will also have opportunities to contribute to peer-reviewed publications and shape next-generation AI-assisted discovery workflows for KBase.

Requirements

A Bachelor’s Degree (or equivalent knowledge/training) in Computer Science, Engineering, Computational Biology, or a related field and a minimum of 5 years of relevant work experience in AI agent frameworks, data engineering, and software development in service of scientific research or an equivalent combination of education and experience.
Experience designing and implementing intelligent agents for scientific or technical workflows for analyzing biology and environmental data sets.
Experience working with large language models (LLMs) and prompt engineering across multiple providers.
Demonstrated experience with PySpark and data engineering using modern lakehouse architectures, including Delta Lake.
Experience deploying and supporting data- and compute-intensive workflows on high-performance computing (HPC) systems and/or cloud-based research infrastructure.
Demonstrated proficiency in Python and AI agent frameworks, including LangChain, LangGraph, and CrewAI.
Familiarity with machine learning libraries, such as PyTorch, TensorFlow, and Scikit-learn.
Knowledge of open-source collaboration, including GitHub workflows and Agile practices.
Strong understanding of AI agent architecture, including agent state management, evaluation, and benchmarking.
Demonstrated analytical and problem-solving skills, including the ability to identify, troubleshoot, and resolve technical problems of diverse scope where analysis of data requires evaluation of identifiable factors.
Excellent oral and written communication skills, including the ability to organize and present technical information effectively to both technical and non-technical audiences.
Demonstrated interpersonal and collaboration skills, including the ability to work effectively with a variety of scientific, operations, and technical teams.

Nice To Haves

A Master’s Degree (or equivalent knowledge/training) in Computer Science, Engineering, Computational Biology, or a related field and a minimum of 3 years of relevant work experience in AI agent frameworks, data engineering, and software development in service of scientific research or an equivalent combination of education and experience.
Experience with scientific reproducibility practices and metadata standards.
Familiarity with scientific data analysis and visualization techniques.

Responsibilities

Collaborate with the KBase technical lead to design and develop AI agents integrated with KBase interfaces, Apps, and data services.
Build agent-based tools using Python, LangChain, LangGraph, CrewAI, and modern LLMs.
Develop AI agents that interact with the KBase Datastore and Delta Lake/PySpark Lakehouse.
Design agent state management, prompt strategies, and benchmarking to ensure reliability and performance.
Develop robust, tested software for agent orchestration and workflow integration.
Collaborate with the AI/ML team on foundational models and tools for microbial genomics.
Integrate AI capabilities across front-end and back-end systems.
Develop, test, maintain, and document software following team quality standards.
Provide technical guidance and mentorship on AI tools and system integration.