Use hands-on exploration to gain exposure to and familiarity with large-scale datasets, including text, images, and structured data.
Build data engineering pipelines to transform and enrich datasets for generative AI model training and evaluation.
Develop backend cloud engineering infrastructure that enables scalable AI/ML deployment and automation frameworks.
Stay informed on cutting-edge applications and tools in generative AI, AI Operations, and cloud computing, and present insights to the team through trainings and discussions.
Develop code-based automated data pipelines capable of processing large volumes of multimodal data for AI applications.
Contribute to the design and implementation of AI Agents and authoring tools that enable smarter, more efficient content creation and management processes.

Requirements

Currently pursuing a degree in Computer Science, Data Science, Bioinformatics, Engineering, Digital Health, or a related field.
Strong understanding of core concepts in Statistics, Data Science, or Computer Science, as demonstrated through current or prior coursework.
Excellent written and verbal communication skills, with the ability to convey technical and non-technical concepts effectively.
Familiarity with exploring and analyzing large datasets.
A genuine interest in innovative research in Digital Health and Real World Data, along with a desire to gain domain knowledge in the field.
Experience with Git version control and writing code in Python.
Background or coursework in computer science, information systems, biomedical engineering, or life sciences.
Hands-on experience through internships or projects involving data engineering and data science.
Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Hugging Face), Dev Ops tools (e.g Jenkins, Docker, Kubeflow, SageMaker).
Experience with cloud platforms (AWS, GCP, Azure) and data processing tools (e.g., Apache Spark, Airflow).
Previous independent or classroom research experience on data-focused projects.
Previous experience or interest in developing AI Agents and digital tools for content authoring or automation.

Benefits

Co-Ops/Interns are eligible to participate in Company sponsored employee medical benefits in accordance with the terms of the plan.
Co-Ops and Interns are eligible for the following sick time benefits: up to 40 hours per calendar year; for employees who reside in the State of Washington, up to 56 hours per calendar year.
Co-Ops and Interns are eligible to participate in the Company’s consolidated retirement plan (pension).

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringdata pipelinesgenerative AImachine learningPythonstatisticsdata analysiscloud engineeringAI model trainingAI Agents

Soft Skills

written communicationverbal communicationteam collaborationpresentation skillsresearch skillsproblem-solvingadaptabilitycritical thinkinginterpersonal skillscuriosity