AI/ML Engineer Intern

Cigna Group•Austin, TX

1d•Hybrid

About The Position

Bring your curiosity, engineering skills, and bold ideas to Evernorth Health Services, a division of The Cigna Group. This internship is a foundational development opportunity for emerging AI/ML engineers ready to build scalable solutions for meaningful health care challenges. You will contribute to strategically important work, learn alongside ambitious and compassionate experts, and explore how modern AI, data, cloud, and software engineering can improve health outcomes and business decisions. This is more than a summer project. Solutions developed by our teams have evolved into enterprise capabilities supporting critical business functions, and several projects have contributed to innovative intellectual property and patent filings. You will gain exposure to the full AI lifecycle while building relationships through professional development, networking, and collaboration with experienced engineers, data scientists, architects, and business stakeholders.

Requirements

Pursuing a master's degree or PhD in computer science, statistics, applied mathematics, engineering, operations research, bioinformatics, information systems, computational linguistics, or another quantitative field.
At least 1 year of hands-on machine learning, data engineering, analytics, or software engineering experience gained through coursework, research, internships, or professional projects.
Working knowledge of Python and SQL, with experience preparing data or building technical solutions.
Experience developing or supporting machine learning, data engineering, or software engineering solutions through academic or applied projects.
Ability to explain technical work clearly, collaborate across disciplines, and adapt based on feedback and new information.

Nice To Haves

Experience with scikit-learn, MLlib, TensorFlow, PyTorch, AWS, Apache Spark, Databricks, PySpark, or Spark SQL.
Experience developing APIs with FastAPI or Flask and using CI/CD tools such as Jenkins or GitHub Actions.
Exposure to Docker, Kubernetes, Hive, Scala, HDFS, or other distributed computing and containerization technologies.
Previous software engineering experience and a 3.0 GPA or higher with strength in quantitative coursework.

Responsibilities

Build and improve reusable machine learning frameworks that accelerate model development and production deployment.
Design scalable data pipelines and feature-engineering solutions using Python, SQL, Spark, Databricks, or related technologies.
Develop production-ready APIs, cloud-native services, and intelligent applications that make AI capabilities easier to use across the enterprise.
Contribute to MLOps automation, containerized deployment, monitoring, and reliable engineering practices that improve speed, quality, and repeatability.
Work with large health care datasets, including pharmacy and medical claims, member information, call transcripts, surveys, and web logs, to identify actionable opportunities.
Partner with technical and business stakeholders to translate complex needs into scalable, responsible solutions and communicate outcomes clearly.
Explore generative AI, agentic AI, retrieval-augmented generation, document intelligence, or real-time prediction while seeking feedback and documenting what you learn.