Machine Learning Research Engineer (Hybrid Eligible)

Requisition Id15347

Level: RP02

Overview:

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.

We are seeking a Machine Learning (ML) Research Engineer who will support the development of self-supervised learning methods for large vision-language models to benefit downstream tasks including object detection and counting, semantic segmentation, change detection, damage assessment, and polygonization of geospatial vector geometries across research projects at ORNL. Research activities will include the design of efficient data preprocessing workflows, transforming level-1b large volumes of high-resolution satellite imagery, deployment feature extraction and damage assessment models, enhancing post-processing and validation workflows based on QA/QC outcomes. Handling of sponsor requirements to deliver large-scale AI products derived from satellite imagery. This position resides in the GeoAI Research Group in the Geographic Data Science Section, Geospatial Science and Human Security (GSHS) Division, National Security Sciences Directorate, at ORNL.

As part of our team, you will support research tasks related to optimizing codes for scaling foundation models training, fine-tuning to other downstream tasks, lead polygonization of building footprint vector geometries and advancing to multimodal damage assessment capabilities. The group conducts cutting edge research and publishes on novel ML breakthrough algorithms for large scale geospatial application challenges. Under the guidance of senior research scientists, the selected applicant will take roles on multidisciplinary teams supporting new research and engineering using ORNL's Frontier exascale supercomputer for its dense GPU-based HPC resources to train large GeoAI models and deploy models and create large-scale production datasets for high-impact sponsor missions.

Major Duties/Responsibilities:

• Deploy large-scale production workflows to support high-volume of sponsor imagery feature requirements.
• Develop and implement production workflows for infrustracture damage assessment and change monitoring.
• Handle high-cadence sponsor data deliverables.
• Scaling of deep neural network inference and deploying tools for polygonization of building footprint vector geometries.
• Develop and implement workflows to support large scale self-supervising learning for large vision-language models.
• Collect, process, and analyze large volumes of high-resolution satellite imagery.
• Support the design and implementation of efficient finetuning methods for deploying task specific foundation models.
• Visualize and communicate analysis results via technical reports, and peer-reviewed publications.
• Collaborate with other research and technical professionals on new methods to advance GeoAI for end-to-end multi-modality geospatial data analytics.
• Deliver strong science and engineering artifacts demonstrating research innovation for our sponsors.
• Performing workflow containerization and liasons for stakeholder software deliverables.

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Basic Qualifications:

• MS or PhD in electrical engineering, civil engineering, geoinformation science, or a related field and two (2) years of applied experience (professional or academic lab setting).
• Hands-on experience training machine learning models on Earth observation imagery and HPC infrastructures using GPU accelerators.

• Experience building data-fusion workflows to ingest multi-modality geospatial data.
• Experience using Python or other programming languages to develop AI algorithms in PyTorch computing framework.

Preferred Qualifications:

• Experience working with spatio-temporal datasets and remote sensing imagery.

• Knowledge of distributed computing, quality control and assessment of satellite imagery derived products.
• Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs.

Special Requirements:

• Visa sponsorship is not available for this position.
• This position requires the ability to obtain and maintain a Q clearance.

Security, Credentialing, and Eligibility Requirements:
For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required. Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation.

To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year. This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.

For foreign national candidates:
If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment. Once you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment.

Benefits at ORNL:

ORNL offers competitive pay and benefits programs to attract and retain dedicated people! The laboratory offers many employee benefits, including medical and retirement plans and flexible work hours, to help you and your family live happy and healthy. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also provided for 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.

In addition, we offer a flexible work environment that supports both the organization and the employee. A hybrid/onsite working arrangement may be available with this position.

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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.

If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.

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.