Requisition Id 16166
Overview:
The Programming Systems Group at ORNL seeks a forward‑leaning Postdoctoral Researcher to advance research at the nexus of Agentic AI, high‑productivity programming systems (Mojo, Julia, Rust, Python), and HPC system co‑design. This position is embedded within the larger DOE ASCR ecosystem, with direct relevance to ongoing efforts, and related AI‑for‑HPC thrusts that emphasize modernization of scientific programming languages and workflows.
This aligns with internal project directions emphasizing high‑productivity languages (Python, Julia, Rust) and their emerging competitors/frameworks (Mojo and JAX) for extreme‑scale heterogeneous systems.
The selected researcher will explore how autonomous AI agents and LLM‑driven code generation can co‑evolve with next‑generation language and compiler ecosystems to accelerate scientific software development at scale.
Major Duties/Responsibilities:
- Agentic AI for High‑Productivity Languages:
- Develop multi‑agent reasoning systems that generate, refactor, and validate scientific code written in Mojo, Julia, Rust, and Python.
- Integrate AI‑driven workflows into DOE‑relevant HPC toolchains, leveraging insights from projects in LLM‑driven HPC programming.
- Incorporate feedback from compiler/runtime systems, including MLIR-based ecosystems (Mojo, Julia, Rust).
- Compiler Infrastructure & LLVM/MLIR Integration:
- Extend LLVM/MLIR pipelines to support AI‑guided optimizations across languages (Julia/JACC, Mojo/MLIR, Rust/LLVM).
- Incorporate Enzyme-based automatic differentiation and multi-language IR tooling for AI‑driven analysis.
- High‑Productivity Programming Systems R&D:
- Contribute to DOE goals of enabling performance‑portable high‑productivity languages (Python/Julia/Rust) and evaluate the emerging role these languages and frameworks within scientific workloads.
- Conduct research on language front‑end abstractions, mixed‑precision modeling, heterogeneous parallelism, and MLIR-level transformations.
- HPC System Co‑Design:
- Work with domain scientists and hardware architects to establish how agentic AI and these languages co‑design with heterogeneous HPC systems (CPUs, GPUs, PIM, AI accelerators).
- Study performance and portability tradeoffs, leveraging comparative research across Mojo, Julia, Rust, and vendor toolchains.
Basic Qualifications:
- Ph.D. in Computer Science, Computer Engineering, or related field.
- Experience with LLMs or agentic AI frameworks applied to compilers or code generation.
- Proficiency in at least one high‑productivity scientific language (Julia, Rust, Python, Mojo) and associated compiler/runtime ecosystems.
- Strong background in HPC programming (C++, MPI, CUDA/HIP/ROCm).
Preferred Qualifications:
- Familiarity with LLVM/MLIR development and multi‑language IR ecosystems.
- Background in formal methods or automated reasoning.
- Experience with performance modeling, static analysis, or PIM/heterogeneous architecture research.
- Knowledge of large-scale scientific computing applications and algorithms (sparse linear system solvers, dense matrix eigenvalue problems, and large-scale graph analysis).
Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.
Letters of Recommendation: References are required.
Instructions to upload documents to your candidate profile:
- Login to your account via jobs.ornl.gov
- View Profile
- Under the My Documents section, select Add a Document
Special Requirements:
- HSPD-12 PIV badge: This position requires the ability to obtain and maintain an HSPD-12 PIV badge.
About ORNL:
As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation.
ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well-being of you and your family. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also available for added convenience.
Other benefits include the following: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life Insurance, Disability Benefits, Generous Vacation and Holidays, Parental Leave, Legal Insurance with Identity Theft Protection, Employee Assistance Plan, Flexible Spending Accounts, Health Savings Accounts, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts.
If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: ORNLRecruiting@ornl.gov.
This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.
We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.
ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.
