AI / Machine Learning Summer Intern (Graduate level)

Position Summary:

Join our mission toinfusecutting-edgeAI/ML/GenAIintopharmacy benefits as aMachine Learning Engineering Intern.We are looking foragraduate studentswithartificial intelligence /machine learningspecializationto join us ininvestigatingAI capabilitiesand tools to improve productivity and createmagical experiences in our products and services.

Inthis role, you will collaborate with researchers, engineers, and program/project managers to tackle innovative challenges. You will also receive technical mentorship and guidance that allows you to learn new things every day, gain practical skills, build real world experience, develop a greater understanding ofthe healthcare techindustry, and form valuable connections. Together, you and your team will partner to design and implement an innovative solution for a Machine Learning problem that is meaningful. At the end of your internship, you will have the opportunity to meet and present your work to leadership.

Responsibilities:

• Investigate and experiment with tools and methodologiesleveragingArtificial Intelligence (AI)andmachinelearning (ML) solutions in the fields of Document understanding, Search andQnA,GenAI,Virtual Agents, etc.

• Work with senior technical staff to develop improvements to models to generatenew contentusing machine learning models in a secure, well-tested, and reasonably performant way.

• Applytoolsand methodologiesfor large-scale data processing and ETLand contribute to extracting insights from data to help guide ML systems development

• Ensure the integrity and robustness of ML solutions byexecutingautomated testing and validation processesestablishedwithin the team.

• Documentandcommunicatedevelopment processes andimplementationdetails with peers and supervisors.

• Ensure the security and compliance of healthcare data, adhering to HIPAA regulations.
• Responsible for adherence to the Capital Rx Code of Conduct including reporting of noncompliance.

RequiredQualifications:

• Working toward a masters or doctoral degree in computer science, engineering, machine learning, applied mathematics, or equivalent.

• Goodunderstandingor having completed course workof Machine Learning fundamentals such as Deep Neural Networks, Transformer-based LLM and MLMs, Boostedor standard decisiontrees, RAG,etc.

• Basic understandingof OOP, Design Patterns, efficient algorithms, and software developmentlifecycles.

• Decentproficiencyin Python.

• Goodproblem-solving skills, attention to detail,anda strong capacity for logical thinking.

• Some experience crafting, conducting, analyzing, and interpreting experiments and investigations is a plus.

• Good written communication skills that enable collaboration in a remoteand fast-pacedenvironment.