Interwell Health
Machine Learning Engineer
About this role
Interwell Health, a kidney care management company, seeks a Machine Learning Engineer to develop and deploy end-to-end ML solutions in a remote role. You'll build scalable ML systems, establish MLOps frameworks, and collaborate with engineers and clinicians to create healthcare AI products that improve patient outcomes.
What you'll do
- Design and implement complete ML solutions from requirements through deployment and monitoring
- Build and maintain MLOps pipelines with CI/CD integration, drift detection, and automated retraining
- Monitor production model performance and address issues with remediation strategies
- Collaborate with engineers, product managers, and clinical staff on new ML products
- Develop API integrations between cloud systems and ML services
- Participate in architectural discussions to ensure compliance and scalability
What they're looking for
- Python for production ML (testing, packaging, type hints, linting)
- SQL for analytical and production workloads
- MLOps and pipeline development (CI/CD, model registry, feature stores)
- Distributed computing and cloud platforms (Spark, Databricks, Azure, AWS, GCP)
- Feature engineering, model development, calibration, and deployment
- LLM and prompt engineering capabilities
- Debugging and optimization across data and ML workflows
- Software engineering best practices for AI/ML systems
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Interwell Health
Interwell Health is a kidney care management company that develops healthcare AI products to improve patient outcomes. The company is hiring Machine Learning Engineers to build scalable ML systems, establish MLOps frameworks, and collaborate with clinical and engineering teams on end-to-end solutions.
View all jobs at Interwell HealthLikely interview questions
- Walk us through a complete end-to-end ML project you've deployed to production. How did you handle feature engineering, model calibration, and what monitoring did you set up?
- Describe your experience building MLOps pipelines. What tools have you used for CI/CD, model registry, and drift detection, and how did you automate retraining?