Senior ML Ops Engineer

Circadia Health • El Segundo, CA

3d • Onsite

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, predictive models, and enterprise integrations to monitor patients and detect adverse events. The company's technology currently impacts over 40,000 post-acute patients daily and is supported by leading investors. This role is crucial as the ML Ops Engineer will own the pipelines, production pathways, and monitoring systems for the company's predictive models, which have significant clinical implications.

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.
• Experience with 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.
• Experience in healthcare, medical devices, or clinical data systems.
• Significant open-source contributions, systems that have outlived your tenure, or a high-bar engineering background.

Responsibilities

• Own pipeline orchestration, including training, evaluation, and deployment workflows in Airflow, with automated retraining, promotion, and failure recovery.
• Manage deployment and release of models onto the AWS platform (including Batch), incorporating versioning and rollback through MLflow, and progressing towards shadow and canary releases.
• Ensure tracking and lineage using MLflow registry, establishing conventions for artifacts and metadata, and implementing dataset versioning for reproducible training runs.
• Build and maintain monitoring and drift detection for models, focusing on prediction quality and degradation alerting for clinically consequential models.
• Manage ML compute and costs on AWS, utilizing infrastructure-as-code and cost optimization strategies.
• Contribute to model development alongside the ML engineering team as a secondary focus.
• Ensure 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed