• Build and maintain CI/CD pipelines for machine learning, focusing on automated testing, model deployment, and version control (using tools like MLflow or Git). • Deploy ML models as scalable APIs and microservices, ensuring they meet performance and latency requirements for clinical use. • Implement basic monitoring tools to track model performance, data drift, and system health in production. • Develop and optimize ETL processes to transform healthcare data (FHIR, HL7) into clean, usable datasets for model training and inference. • Help build and maintain feature stores and data layers that ensure consistency between training and production environments. • Work closely with backend teams to integrate ML outputs into our core healthcare applications. • Write clean, maintainable, and well-documented Python code. • Participate in code reviews to ensure system reliability. • Use Docker and Kubernetes to package and orchestrate ML workloads across different environments. • Follow established protocols to ensure all data handling and deployments meet HIPAA and HITRUST security standards.

• Work on Wayfinding projects that impact the business and contribute to Transcarent’s mission of better health and care • You'll gain exposure to how a real engineering team works, from sprint planning to shipping code • You’ll be exposed to experienced engineers from all backgrounds (web development, mobile, API, Salesforce, etc.) • You'll have a formal mentor on your team guiding you throughout your internship period

• Propose analytical approaches and predictive models to optimize outcomes. • Develop and validate Machine Learning models. • Design experiments and validate hypotheses based on evidence. • Prepare and analyze data for model development. • Monitor the performance of implemented solutions and recommend continuous improvements. • Collaborate with business, product, and technology teams.

• Work directly with CV researchers to understand their goals, review their code, and engineer it for reliability and performance at scale. • Profile and optimize performance-sensitive code across both training and real-time inference. • Identify patterns across research efforts and propose standardized, composable abstractions. • Collaborate with MLOps and backend engineers to translate recurring research pain points into platform investments through design documents and architecture proposals. • Guide researchers toward production-oriented engineering practices regarding testability, modularity, and performance profiling.