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Oak Ridge National Laboratory · 2 hours ago

Machine Learning Research Engineer (Hybrid Eligible)

Oak Ridge, TN

Full-time

Hybrid

Entry, Mid Level

2+ years exp

Oak Ridge National Laboratory (ORNL) is a U.S. Department of Energy national laboratory with a legacy of addressing pressing challenges. They are seeking a Machine Learning Research Engineer to develop self-supervised learning methods for large vision-language models and support various geospatial research projects.

Advanced MaterialsClean EnergyEnergyEnergy ManagementManufacturingNuclearRenewable Energy

No H1B

Security Clearance Required

U.S. Citizen Only

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

Qualification

Machine LearningPythonPyTorchHPC infrastructureGeospatial dataCollaborationAdaptabilityCommunication

Required

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

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