Machine Learning Engineer

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 Less than 10% 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. 0 years of related experience with a Bachelor's degree in Computer Science, Biomedical Engineering, or related Please refer to our Research Faculty Technical Level Guidelines for minimum requirements at the higher levels. 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 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. 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. The University System of Georgia is comprised of our 26 institutions of higher education and learning as well as the System Office. 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