Senior ML Ops Engineer
About The Position
Circadia Health is a growth-stage healthcare AI company focused on preventing avoidable hospitalizations and transforming senior-care operations. Their platform integrates contactless sensing for monitoring, predictive models for early detection of adverse events, and enterprise integrations for seamless workflow implementation. The technology currently serves over 40,000 post-acute patients daily and is supported by leading healthcare and AI investors. This role is critical because the models directly influence care decisions, and any degradation can have serious clinical consequences. The Senior ML Ops Engineer will be responsible for the entire lifecycle of these models, from pipelines to production and monitoring, ensuring their reliability and accuracy.
Requirements
- 4+ years in MLOps, ML engineering, DevOps, or a closely related infrastructure role.
- Strong Python skills for pipeline development, tooling, and automation.
- Hands-on experience with Airflow and a model registry such as MLflow.
- Experience deploying and operating ML workloads on AWS (Batch, EC2, S3, IAM, CloudWatch).
- Proficiency in containerization, infrastructure-as-code, SQL, and Snowflake.
- Experience building monitoring and alerting for production systems.
- Sufficient model development experience to contribute alongside ML engineers.
Nice To Haves
- Experience with model serving frameworks or data versioning tools.
- Background in healthcare, medical devices, or clinical data systems.
- Significant open-source contributions, experience with systems that outlived your tenure, or a high-bar engineering background.
Responsibilities
- Pipeline orchestration, including training, evaluation, and deployment workflows in Airflow, with automated retraining, promotion, and failure recovery.
- Deployment and release of models onto the AWS platform, including Batch, with versioning and rollback through MLflow, progressing towards shadow and canary releases.
- Tracking and lineage using MLflow registry, establishing conventions for artifacts and metadata, and dataset versioning for reproducible training runs.
- Monitoring and drift detection for models, focusing on prediction quality and degradation alerting, especially where degradation is clinically consequential.
- Managing ML compute and cost on AWS for training and inference, utilizing infrastructure-as-code and cost optimization strategies.
- Contributing to model development alongside the ML engineering team as a secondary focus.
- Ensuring HIPAA and SOC 2 compliance across pipelines, with proper handling of PHI in training data, artifacts, and outputs.
Benefits
- Meaningful employee stock options
- 100% company-paid medical, dental, and vision coverage
- 401(k)
- Competitive time off with pay policies including vacation, sick days, and company holidays
- Impact: your work will influence care decisions for tens of thousands of seniors every day.
- Culture: hard-working, mission-driven, and collaborative — with weekly and monthly social events like yoga, beach bonfires, and Wednesday/Friday team lunches.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
