Machine Learning Engineer

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. Project/Unit Description The Health Emerging & Advanced Technologies (HEAT) Division is dedicated to improving individual and population health through advances in the capture, analysis, and standards-based, interoperable communication of health data. Our goals are 1) to advance foundational technologies in health data analytics; 2) to promote interoperability and open-source platforms in health; 3) to support healthcare partners in maximizing the value of their data resources; and 4) to foster scalable and secure health research locally and globally. The Health Interoperability and Security Branch (HISB) in the HEAT division is dedicated to solving applied research problems in public health, both nationally and specifically in the state of Georgia. Researchers in HISB apply health interoperability and security solutions to integrating health systems and data for a wide variety of purposes, including clinical care, research, public health improvement, cost and quality, and consumer-based health applications. 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 Develop software products using software tools such as R, Python, C++, C and/or Julia. Deploy machine learning models utilizing existing tooling. Conduct research in a multi-disciplined team environment. Additional Responsibilities Design and implement agentic AI systems that coordinate complex health data workflows across multiple systems and stakeholders. This includes developing autonomous agents that can orchestrate data pipelines, manage interoperability tasks, and coordinate multi-step processes for health information exchange with minimal human intervention. Develop multi-agent AI architectures that can collaborate to solve complex health interoperability challenges, such as automated data mapping, quality assurance, error detection and correction, and standards compliance verification across distributed health systems. Make a meaningful impact on health interoperability research by developing and deploying AI/ML solutions that enhance data exchange, integration, and standardization across disparate health systems. This includes working with FHIR, HL7, and other healthcare interoperability standards to enable seamless data flow between clinical, research, and public health systems. Engage in health research by exploring literature in machine learning, artificial intelligence, and computer science to find innovative solutions for real-world health interoperability problems. Develop and apply novel solutions to address research challenges, including the use of machine learning models, large language models, agentic AI systems, and workflow orchestration frameworks for tasks such as clinical data extraction, semantic interoperability, automated data quality assessment, data mapping, validation, and standards conversion. Collaborate with fellow researchers in multidisciplinary research and review work for quality improvement. Communicate verbal and written feedback in a clear and effective manner. Required Minimum Qualifications Candidates currently enrolled in an accredited degree program relevant to this position will be considered. The candidate must have a graduation date of no later than August 2026. Strong proficiency in Python. Strong machine learning, artificial intelligence, computer science, or software engineering background. Experience developing, deploying, or working with APIs and data exchange systems. Experience communicating and presenting technical findings, both written and verbal. Ability to work collaboratively in a multidisciplinary team environment. Preferred Qualifications Experience with AI workflow orchestration tools and frameworks (e.g., LangChain, LlamaIndex, Apache Airflow). Experience building agentic AI systems, autonomous workflows, or multi-agent architectures. Experience implementing AI observability, monitoring, and evaluation frameworks for ML systems. Experience with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes). Experience with health interoperability standards and frameworks such as FHIR, HL7, DICOM, and healthcare terminologies (LOINC, SNOMED CT, ICD, RxNorm). Experience with version control systems (Git) and CI/CD practices for ML deployment. Knowledge of data security, encryption, federated learning, differential privacy, or other privacy-enhancing technologies. Experience with RESTful APIs, microservices architecture, and distributed systems. Travel Requirements 10% travel. Education and Length of Experience 0 years of related experience with a Bachelor's degree in Computer Science, Biomedical Engineering, or related. 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 None. 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: 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. 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