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.
Past projects have included Spark and Databricks feature-engineering platforms; AI/ML experimentation frameworks; MLOps and deployment automation; agentic and generative AI applications; enterprise chatbots; retrieval-augmented generation and document intelligence platforms; and real-time prediction services.
Required Qualifications
- 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.
Preferred Qualifications
- 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.
Additional Information
Location: This internship follows a hybrid schedule with three days per week in the Austin, TX; Morris Plains, NJ; or St. Louis, MO office.
Compensation: Hourly pay ranges from $35.00-$43.00, based on degree program and year of study.
Schedule: This is a full-time, 12-week summer internship working 40 hours per week beginning in May 2027.
Work Authorization: Candidates must be authorized to work in the United States and not require current or future employment sponsorship.
If you will be working at home occasionally or permanently, the internet connection must be obtained through a cable broadband or fiber optic internet service provider with speeds of at least 10Mbps download/5Mbps upload.
