Machine Learning Research Engineer (Hybrid Eligible)

Requisition Id14225

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, visual question answering, semantic segmentation, change detection and polygonization of geospatial vector geometries across research projects at ORNL. 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 several downstream tasks and lead polygonization of building footprint vector geometries. The group conducts cutting edge research and publishes on novel ML-based solutions to large scale geospatial application challenges. Research activities include the design of efficient ML workflows using high performance computing (HPC) environments, preprocessing and transforming large volumes of satellite imagery, and conducting post-processing and validations of model of outcomes. Under the guidance of senior research scientists, the selected applicant will take roles on multidisciplinary teams supporting ground breaking research and engineering with large-scale distributed ML workflows, using ORNL's Frontier exascale supercomputer for its dense GPU-based HPC resources to train large GeoAI models.

Major Duties/Responsibilities:

• Develop and implement new methods 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 satellite imagery

• Support the design and implementation of efficient foundation models finetuning methods.

• 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.

• Deliver ORNL's mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace - in how we treat one another, work together, and measure success

Basic Qualifications:

• MS in electrical engineering, civil engineering, geoinformation science, or a related field and two (2) years of applied experience (professional or academic lab setting). An equivalent combination of education and experience may be considered.

• Hands-on experience training machine learning models on 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 and uncertainty quantification.

• 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 available for this position.

• This position requires the ability to obtain and maintain an HSPD-12 PIV badge.

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.

This position is located in Oak Ridge, Tennessee and requires onsite presence. 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, which provides flexibility to work periodically from your home, while reporting onsite to the Oak Ridge, Tennessee location on a weekly and regular basis.

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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.