AI​/ML Engineer - ELSYS - Colorado Springs

Overview

The Georgia Tech Research Institute (GTRI) is the nonprofit, applied research division of the Georgia Institute of Technology (Georgia Tech). Founded in 1934 as the Engineering Experiment Station, GTRI has grown to more than 2,900 employees, supporting eight laboratories in over 20 locations around the country and performing more than $940 million of problem‑solving research annually for government and industry. GTRI's renowned researchers combine science, engineering, economics, policy, and technical expertise to solve complex problems for the U.S. federal government, state, and industry.

Georgia Tech's mission is to develop leaders who advance technology and improve the human condition. The Institute has nine key values that are foundational to everything we do:

• Students are our top priority.
• We strive for excellence.
• We thrive on diversity.
• We celebrate collaboration.
• We champion innovation.
• We safeguard freedom of inquiry and expression.
• We nurture the wellbeing of our community.
• We act ethically.
• We are responsible stewards.

Over the next decade, Georgia Tech will become an example of inclusive innovation, a leading technological research university of unmatched scale, relentlessly committed to serving the public good; breaking new ground in addressing the biggest local, national, and global challenges and opportunities of our time; making technology broadly accessible; and developing exceptional, principled leaders from all backgrounds ready to produce novel ideas and create solutions with real human impact.

The Electronic Systems Laboratory (ELSYS) at the Georgia Tech Research Institute (GTRI) is seeking an AI/ML Engineer to support the United States Space Force (USSF) Space Systems Command (SSC). This position directly supports the USSF portfolio, specifically the Operational Test and Training Infrastructure (OTTI) / System Delta 81.

This role centers on "physical AI" for space systems—AI tightly coupled to high‑fidelity, physics‑based satellite simulations and live operational data. The selected candidate will help build and integrate digital twin‑style environments (e.g., NVIDIA‑based simulation stacks) that remain time‑aligned with real satellite telemetry and sensor data, enabling realistic test, training, and evaluation of AI capabilities for space operations.

While the broader AI/ML engineering discipline encompasses algorithm development, this specific position focuses heavily on the foundational data, interoperability, and simulation architectures required to deploy and evaluate advanced AI and Large Language Model (LLM) capabilities within DoD space simulation and live environments. Working in direct support of OTTI physical AI and emerging technology efforts, this role is responsible for engineering the critical data infrastructure, semantic ontologies, robust API abstraction layers, and advanced data pipelines that connect digital twins, AI training/evaluation workflows, and live systems.

The Artificial Intelligence/Machine Learning (AI/ML) Engineer develops AI/ML algorithms, cloud computing, and/or heterogeneous distributed computing infrastructures to support the deployment of AI/ML applications. The AI/ML Engineer also researches the mathematical foundations and frameworks for nonlinear systems characterized by time‑varying and emerging dynamics of evolving or adaptive systems. The AI/ML Engineer develops technical solutions at the leading edge of Artificial Intelligence, Machine Learning, Genetic Programming, Computer Vision, and advanced data processing, filtering, and fusion techniques in high‑performance computing and distributed heterogeneous computing environments.

The AI/ML Engineer writes parallel processing programs to deploy ML models developed by data scientists into more complex systems. The AI/ML Engineer has familiarity with state‑of‑the‑art, open‑source software frameworks and high‑performance computing accelerators for machine learning. When conducting research, the AI/ML Engineer leverages the most recent advances in statistical analysis of large data sets to advance state‑of‑the‑art…