About this opportunity
CertifyOS is hiring an AI Intern for a 6-month contract to build, test, and deploy machine learning services on its provider data platform. This is a fully remote position focused on production engineering, not academic research. You will own features end-to-end, from working with stakeholders to deploying services on Google Cloud Platform (GCP). CertifyOS builds the data infrastructure that powers modern healthcare. The company's API-first platform automates provider licensing, enrollment, credentialing, and network monitoring by connecting directly to hundreds of primary data sources. Healthcare organizations use this to maintain accurate, compliant, and reliable provider networks at scale. The company is backed by leading investors and built by a team with deep experience in provider data systems. Their vision is simple: one API, one provider ID, frictionless provider data. The role sits at the intersection of software engineering and machine learning. You will write production-ready Python code, design evaluation pipelines, and operate ML services on GCP tools like Cloud Run, GKE, Cloud Functions, Pub/Sub, BigQuery, and Cloud Storage. The focus is on strong engineering, testing, and robust evaluation rather than novel model architectures. You will collaborate with product, operations, engineering, and data teams to clarify requirements and iterate on solutions. Working remotely means you set your own schedule, but you will need to communicate clearly across time zones. The team values authenticity, accountability, collaboration, results, and openness to feedback. This is a high-ownership environment where you drive projects with limited supervision. If you have experience deploying ML workloads on GCP and writing clean, tested Python code, this is a chance to work on infrastructure that impacts millions of patients.
Responsibilities
- 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
Benefits
- 100% coverage of health, dental, and vision insurance premiums for employees
- Unlimited PTO for US-based team members, with at least two weeks off each year to recharge
- Health insurance, statutory leave benefits, and additional wellness (menstrual) leave for women in India
- Fully remote position
- Pay transparency culture where compensation conversations are encouraged and respected
- Equal opportunity employer committed to an inclusive environment
Eligibility
- 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: experience writing code in Java
- Bonus: experience building or maintaining data pipelines or ETL jobs on GCP
- Bonus: experience working with healthcare data, compliance requirements, or PII
- Bonus: experience using experiment tracking and evaluation tools such as MLflow, Weights & Biases, or custom dashboards
How to Apply
- Prepare your resume and any relevant portfolio or GitHub links that demonstrate your ML engineering experience.
- Email your application to [email protected] if you require reasonable accommodations during the application process.
- Click the Apply button below to submit your application.
Ready to start your application?
Submit your materials before the official deadline.
Verify all official guidelines and eligibility criteria directly with the host institution before submitting. Prepare your transcript evaluation, referee letters, and motivational statement early to avoid deadline bottleneck delays.
Official Application Strategy & Recommended Tools
Selection Committee Priorities
- •Academic/Professional Alignment: Ensure your statement explicitly ties your past work in to Certifyos's strategic goals.
- •Leadership Evidence: Selection panels heavily weigh demonstrated initiative, community impact, or research output.
- •Financial & Language Prerequisites: Confirm if Medium of Instruction (MOI) certificates can substitute for formal standardized language test fees.
