Research Fellow - Artificial Intelligence/Machine Learning

Essential Functions

• Design, develop, and deploy machine learning and deep learning models for applied research projects.
• Manage multiple concurrent projects, ensuring timely delivery and alignment with stakeholder needs.
• Build and maintain MLOps pipelines for model training, evaluation, versioning, and deployment.
• Implement scalable ML infrastructure across cloud platforms (e.g., AWS, GCP, Azure) and on-premises environments.
• Develop APIs and integration layers to embed ML capabilities into applications and research workflows.
• Collaborate with researchers and non-technical stakeholders to translate research questions into technical solutions.
• Document technical implementations, model architectures, and research methodologies to ensure reproducibility.
• Stay current with emerging AI/ML techniques and evaluate their applicability to Center projects.

Other Functions

• Other duties as assigned.

Minimum Qualifications

• Bachelor's degree or higher in Computer Science, Data Science, Machine Learning, or a related field.
• Strong programming expertise in Python, with experience in ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
• Demonstrated experience deploying ML models to production environments.
• Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, DVC, Docker, CI/CD for ML).
• Experience with cloud-based ML services and infrastructure (e.g., AWS SageMaker, GCP Vertex AI, Azure ML).
• Ability to manage multiple projects and guide junior engineers or collaborators.
• Excellent written and verbal communication skills for working with technical and non-technical stakeholders.
• Demonstrated ability to work independently and solve complex technical challenges.

Preferred Qualifications

• Experience with LLMs, generative AI, and prompt engineering.
• Familiarity with NLP, computer vision, or time-series modeling.
• Experience with distributed computing frameworks (e.g., Spark, Ray, Dask).
• Background in data engineering and pipeline orchestration (e.g., Airflow, Prefect).
• Prior experience in academic or research-focused settings.
• Experience mentoring or leading technical teams.
• Familiarity with responsible AI practices and model interpretability.

Working Conditions

• Work is performed in a standard office or indoor university environment and involves minimal physical exertion.
• Work Schedule and Work Arrangement
• Typical office hours between 8:00 AM and 5:00 PM.

Special Instructions for Applicants

• Alongside your application, please include CV or résumé detailing your academic and professional background, a cover letter describing your relevant experience and research interests, and the contact information for three (3) professional references.