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About This Role
- 500985
- Colorado Springs, Colorado
- Aerospace
- Algorithm Development
- Artificial Intelligence
- Researchers
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### 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 and Values
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:
1\. Students are our top priority.
2\. We strive for excellence.
3\. We thrive on diversity.
4\. We celebrate collaboration.
5\. We champion innovation.
6\. We safeguard freedom of inquiry and expression.
7\. We nurture the wellbeing of our community.
8\. We act ethically.
9\. 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.
### Project/Unit Description
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.
### Job Purpose
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 automated sensor and data processing for a broad range of intelligent and sensor\-enabled systems.
### Key Responsibilities
- Design complex system architectures (e.g., high\-performance computing clusters, networks, chipsets, GPUs) based on available hardware (e.g., embedded systems, cloud, on\-premise, etc.)
- Lead a team of engineers responsible for system deployment
- Develop novel algorithms and methodologies
- Engage with sponsors to understand and meet system requirements
- Serve as the primary author on technical reports and proposals
### Additional Responsibilities
- Bridge the gap between high\-fidelity space simulation environments, AI training workflows, and live systems (e.g., NVIDIA\-based digital twin environments) to operationalize AI in widely interoperable test, training, and operational contexts
- Architect and develop API abstraction layers to consolidate and streamline data access across disparate relational, graph, and simulation data services for downstream AI/ML applications
- Design and implement semantic ontologies and data models to standardize complex military datasets, such as the Unified Data Library (UDL), and to enable seamless integration with AI and Large Language Model (LLM) capabilities and orchestration frameworks (e.g., Model Context Protocol)
- Build, optimize, and maintain robust data pipelines that connect live space data sources, UDL datasets, and simulation environments, including ingestion, knowledge graph development, transformation, and vectorization to support Retrieval\-Augmented Generation (RAG) and other AI inference workflows
- Translate legacy data systems and unstructured data stores into interoperable, AI\-ready formats that can both drive and be driven by physics\-based simulations in support of OTTI initiatives
- Develop and maintain comprehensive technical documentation for data schemas, API endpoints, simulation\-AI integration patterns, and AI system interoperability frameworks
- Evaluate and integrate emerging commercial and open\-source simulation, AI, and data engineering technologies into secure DoD/USSF environments, in coordination with OTTI stakeholders and system architects
### Required Minimum Qualifications
- Experience developing software in Python for data processing, backend services, or basic machine learning workflows
- Experience working with at least one relational database (e.g., PostgreSQL, MySQL) including schema design and querying
- Experience building and consuming web APIs (e.g., REST or GraphQL) in a production or research environment
- Experience integrating, deploying, testing, and tuning large language models
- Experience implementing data pipelines that perform ingestion, transformation, and preparation of data for analytics or ML (e.g., ETL/ELT workflows)
- Experience with at least one common ML or numerical computing framework (e.g., PyTorch, TensorFlow, Scikit\-learn, NumPy) in a coursework, research, or professional context
- Experience using software version control (e.g., Git) in a collaborative environment
### Preferred Qualifications
- Active TS/SCI Clearance
- Experience working with GPU\-accelerated simulation or digital twin environments for robotics, aerospace, or space systems (e.g., NVIDIA Omniverse, Isaac Sim, or similar tools)
- Experience integrating or querying data from the Unified Data Library (UDL) or other large\-scale Department of Defense (DoD) or Intelligence Community data repositories
- Familiarity with Space Domain Awareness (SDA), space operations, or Operational Test and Training Infrastructure (OTTI) concepts
- Experience designing or integrating data models and ontologies for complex, multi\-source operational data (e.g., sensor data, telemetry, simulation outputs)
- Experience utilizing Large Language Model (LLM) orchestration frameworks (e.g., LangChain, LlamaIndex) for Retrieval\-Augmented Generation (RAG) or tool\-using agents
- Experience deploying applications or services within DoD DevSecOps platforms (e.g., Platform One) or government cloud environments (e.g., AWS GovCloud, Azure Government)
- Familiarity with Semantic Web standards (e.g., RDF, OWL, SPARQL) for building and mapping robust data ontologies and knowledge graphs
- Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes) in secure or resource\-constrained environments
### Travel Requirements
10% \- 25% travel
### Education and Length of Experience
This position vacancy is an open\-rank announcement. The final job offer will be dependent on candidate qualifications in alignment with Research Faculty Extension Professional ranks as outlined in section 3\.2\.1 of the Georgia Tech Faculty Handbook
- 5 years of related experience with a Bachelor’s degree in Computer Science, Software Engineering, Robotics, Computer Engineering, Data Science, Electrical Engineering, Mathematics, Physics, or a related degree
- 3 years of related experience with a Masters’ degree in Computer Science, Software Engineering, Robotics, Computer Engineering, Data Science, Electrical Engineering, Mathematics, Physics, or a related degree
- 0 years of related experience with a Ph.D. in Computer Science, Software Engineering, Robotics, Computer Engineering, Data Science, Electrical Engineering, Mathematics, Physics, or a related degree
### U.S. Citizenship Requirements
Due to our research contracts with the U.S. federal government, candidates for this position must be U.S. Citizens.
### Clearance Type Required
Candidates must be able to obtain and maintain an active security clearance.
### Benefits at GTRI
Comprehensive information on currently offered GTRI benefits, including Health \& Welfare, Retirement Plans, Tuition Reimbursement, Time Off, and Professional Development, can be found through this link: https://benefits.hr.gatech.edu/.
### Equal Employment Opportunity
The Georgia Institute of Technology (Georgia Tech) is an Equal Employment Opportunity Employer. The Institute is committed to maintaining a fair and respectful environment for all. To that end, and in accordance with federal and state law, Board of Regents policy, and Institute policy, Georgia Tech provides equal opportunity to all faculty, staff, students, and all other members of the Georgia Tech community, including applicants for admission and/or employment, contractors, volunteers, and participants in institutional programs, activities, or services. Georgia Tech complies with all applicable laws and regulations governing equal opportunity in the workplace and in educational activities.
Equal opportunity and decisions based on merit are fundamental values of the University System of Georgia (“USG”) and Georgia Tech. Georgia Tech prohibits discrimination, including discriminatory harassment, on the basis of an individual’s race, ethnicity, ancestry, color, religion, sex (including pregnancy), national origin, age, disability, genetics, or veteran status in its programs, activities, employment, and admissions. Further, Georgia Tech prohibits citizenship status, immigration status, and national origin discrimination in hiring, firing, and recruitment, except where such restrictions are required in order to comply with law, regulation, executive order, or Attorney General directive, or where they are required by Federal, State, or local government contract.
### USG Core Values Statement
The University System of Georgia is comprised of our 26 institutions of higher education and learning as well as the System Office. Our USG Statement of Core Values are Integrity, Excellence, Accountability, and Respect. These values serve as the foundation for all that we do as an organization, and each USG community member is responsible for demonstrating and upholding these standards. More details on the USG Statement of Core Values and Code of Conduct are available in USG Board Policy 8\.2\.18\.1\.2 and can be found on\-line at https://www.usg.edu/policymanual/section8/C224/\#p8\.2\.18\_personnel\_conduct.
Additionally, USG supports Freedom of Expression as stated in Board Policy 6\.5 Freedom of Expression and Academic Freedom found on\-line at https://www.usg.edu/policymanual/section6/C2653\.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Georgia Tech, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Georgia Tech AI Hiring
Georgia Tech has 3 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Positions span Atlanta, GA, US, Colorado Springs, CO, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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