Research Associate, Agentic Workflows

Oak Ridge National Laboratory•Oak Ridge, TN

1d•Hybrid

About The Position

Oak Ridge National Laboratory (ORNL) is seeking a Research Scientist to support agentic workflow orchestration and cross-facility integration in support of the Genesis Mission, the American Science Cloud, and the Oak Ridge Leadership Computing Facility (OLCF). Working within the Workflows and Ecosystem Services (WES) Group at the National Center for Computational Sciences (NCCS), you will help design, implement, and deploy agentic workflows and frameworks that bridge leadership computing resources, instruments, and cloud platforms, working under the guidance of senior technical staff. You will collaborate with scientists, facility operators, and DOE laboratory partners to lower barriers to scalable, automated scientific computing. The WES Group designs and implements workflow, data, and ecosystem technologies that enable reliable, automated execution of scientific applications across heterogeneous, geographically distributed computing environments, integrating compute, storage, networking, and visualization resources into cohesive, end-to-end systems. The WES Group sits within the Advanced Technologies section of the National Center for Computational Sciences (NCCS), a division of the Computing and Computational Sciences Directorate (CCSD) at ORNL. NCCS provides state-of-the-art computational and data science infrastructure for technical and scientific professionals and hosts the Oak Ridge Leadership Computing Facility (OLCF), a DOE Office of Science User Facility that is home to Frontier, the first supercomputer to break the exascale barrier. NCCS staff work at the forefront of leadership computing, enabling scientific discovery for researchers across government, academia, and industry.

Requirements

Ph.D. in Computer Science, Computer Engineering, or a closely related field, or a M.S. or B.S. in a related field with commensurate relevant experience.
Experience designing or operating distributed systems, provenance frameworks, or workflow orchestration frameworks.
Familiarity with or exposure to designing agentic workflows or AI agent frameworks.
At least 1 year of programming experience in Python, with working familiarity in Bash and/or a compiled language (C/C++ or similar).
Experience with Linux-based HPC environments and familiarity with job scheduling systems (e.g., SLURM or PBS).
Strong verbal and written communication skills, with the ability to collaborate across technical and scientific teams.

Nice To Haves

Demonstrated experience with provenance models such as W3C PROV.
Demonstrated experience publishing research in high-impact venues such as SC, IPDPS, HPDC, or IEEE eScience.
Experience working in or coordinating across DOE national laboratory environments, particularly in the context of integrated or cross-facility research infrastructure.
Background in scientific data management, provenance tracking, or metadata systems.
Demonstrated ability to contribute to technical projects and coordinate effectively with multidisciplinary teams.
Familiarity with basic cyber-security principles (e.g., SSH key hygiene, least privilege, network segmentation) as applied to workflow and API design.

Responsibilities

Contribute to the development of runtime data integration and provenance capture systems (e.g., Flowcept) that instrument distributed scientific workflows, ML training pipelines, and agentic systems.
Help implement low-overhead instrumentation and adapters for common execution frameworks, apply provenance data models aligned with community standards (e.g., W3C PROV), and support the query, storage, and streaming layers (e.g., message brokers, document and time-series stores) needed to make workflow telemetry usable at scale.
Support ORNL's participation in cross-facility initiatives (e.g., AmSC, Genesis Mission) that build interoperable provenance and workflow-data capabilities spanning leadership computing, experimental facilities, and cloud platforms.
Help build services that track data lineage, model artifacts, and computational campaigns across the DOE complex, and assist in coordinating with partner laboratories to align schemas, APIs, and metadata standards so that provenance is portable between facilities.
Track milestones and deliverables and contribute to proposals and reports that sustain the portfolio.
Support OLCF platform efforts (e.g., OPAL) by helping translate scientific and programmatic requirements into deployable system designs.
Assist in evaluating architectural trade-offs across orchestration, data management, storage, and security, and work with senior staff and facility operations to help move designs from prototype toward production.
Implement components of agentic workflow architectures that enable autonomous, closed-loop scientific experimentation across HPC and instrument facilities.
Help integrate agentic frameworks with provenance and orchestration layers so that AI agents can plan, execute, monitor, and adapt multi-step workflows while producing auditable, reproducible records of their decisions and tool use.
Apply these patterns to use cases such as autonomous materials discovery, self-steering simulations, and adaptive experimental campaigns.
Contribute to the technical direction and health of open-source research software, including code review, CI/CD, testing, packaging, documentation, and release support.
Help support the external contributor and user community, respond to issues, and help ensure software is deployable beyond ORNL.
Participate in research collaborations with universities, national laboratories, and international partners.
Contribute to joint research directions and help convert collaborative work into shared software and publications.
Collaborate with domain scientists in areas such as materials science, climate science, and autonomous experimentation to translate research objectives into scalable, automated workflow and data-capture solutions.
Provide documentation, training, and direct user support.
Conduct research on provenance, workflow systems, orchestration, and data management for AI-driven science, under the guidance of senior staff.
Publish in peer-reviewed venues and present at conferences (e.g., SC, HPDC, IPDPS, eScience).