About CertifyOS
CertifyOS is building the data infrastructure that powers modern healthcare.
Today, healthcare organizations rely on fragmented and outdated provider data. This creates unnecessary administrative work, regulatory risk, and higher costs across the system. We’re solving that problem.
Our API-first platform automates provider licensing, enrollment, credentialing, and network monitoring by connecting directly to hundreds of primary data sources. We help healthcare organizations maintain accurate, compliant, and reliable provider networks at scale.
Our vision is simple: One API. One provider ID. Frictionless provider data.
We’re backed by leading investors and built by a team with deep experience in provider data systems. At CertifyOS, we value authenticity, accountability, collaboration, results, and openness to feedback. We’re building a high-ownership team focused on solving real infrastructure problems that impact millions of patients.
About the Role:
As an Intern Machine Learning Engineer on a 6-month contract, you will help build, test, and deploy ML-powered services on our provider data platform. This is not a pure research role; the focus is on strong software engineering, testing, and robust evaluation rather than novel model architectures.
You will own features end-to-end by collaborating with stakeholders, implementing production-ready code, designing evaluation pipelines, and deploying services on Google Cloud Platform (GCP). This is a fully remote position.
What You’ll Do:
Design, implement, and maintain ML-driven services and data workflows in Python.
Apply software engineering best practices, including clean code, testing (unit and integration), code reviews, CI/CD, observability, and documentation.
Build and maintain evaluation pipelines and metrics to measure model and system performance in production-like environments.
Deploy and operate ML services on GCP, including tools such as Cloud Run, GKE, Cloud Functions, Pub/Sub, BigQuery, and Cloud Storage.
Troubleshoot and improve existing ML services with a focus on reliability, latency, and correctness.
Collaborate proactively with internal stakeholders across product, operations, engineering, and data teams to clarify requirements and iterate on solutions.
Communicate clearly about trade-offs, risks, timelines, and results to both technical and non-technical audiences.
What You’ll Need:
Experience as a Software Engineer or Machine Learning Engineer.
Strong proficiency in Python and experience building production services.
Hands-on experience deploying and running workloads on Google Cloud Platform.
Expertise in writing and debugging SQL queries.
Strong foundation in software engineering fundamentals, including testing, debugging, version control (Git), CI/CD, and monitoring.
Experience evaluating ML systems by defining metrics, building evaluation datasets, running experiments, and interpreting results.
Ability to work independently, take ownership, and drive projects with limited supervision.
Excellent written and verbal communication skills in English.
Comfort proactively reaching out to internal stakeholders to understand requirements.
Bonus Points If You:
Have experience writing code in Java.
Have built or maintained data pipelines or ETL jobs on GCP.
Have experience working with healthcare data, compliance requirements, or PII.
Have used experiment tracking and evaluation tools such as MLflow, Weights & Biases, or custom dashboards.
Benefits of Working at Certify
At Certify, we’re building with intention and taking care of the people doing the work.
Your well-being matters to us. We provide 100% coverage of health, dental, and vision insurance premiums for employees. Our US-based team benefits from unlimited PTO, with at least two weeks off each year to recharge. In India, employees are supported with health insurance, statutory leave benefits, and additional wellness (menstrual) leave for women.
We are an equal opportunity employer committed to building an inclusive environment where everyone feels valued and empowered to do their best work, and we welcome applicants from all backgrounds and experiences.
If you require reasonable accommodations during the application process, please contact [email protected].
We are also committed to pay transparency and foster an open culture where compensation conversations are encouraged and respected.
