Sr. Machine Learning Software Engineer

Cleerly•Denver, CO

6d•$153,000 - $179,000•Hybrid

About The Position

Cleerly is a healthcare company revolutionizing heart disease diagnosis, treatment, and tracking. Founded in 2017, it's a growing team of engineering, operations, medical affairs, marketing, and sales leaders. Cleerly raised $223M in Series C funding in 2022 and an additional $106M in a Series C extension in December 2024, enabling rapid growth. Most teams work remotely, with access to offices in Denver, Colorado, New York, New York, Dallas, Texas, and Lisbon, Portugal. Some roles may require on-site presence. Cleerly has created a new standard of care for heart disease through value-based, AI-driven precision diagnostic solutions aimed at preventing heart attacks. Their technology quantifies and characterizes atherosclerosis (plaque buildup) in heart arteries, going beyond traditional measures. Cleerly's solutions are backed by over a decade of clinical trials identifying risk factors beyond symptoms. The company emphasizes digital collaboration using Google Workspace, Slack, Confluence/Jira, and Zoom Video. While mostly remote, travel for team meetings and projects is required, typically monthly or quarterly. Some roles may require up to 90% travel. Cleerly is committed to providing safe and effective medical software, adhering to regulatory requirements and continuously improving products and processes to manage risks and ensure compliance throughout the software lifecycle. Understanding the role's importance is critical to achieving Cleerly's quality objectives.

Requirements

7+ years of experience in software engineering for ML production or ML platform delivery.
Hands-on experience deploying ML models via APIs, batch pipelines, or streaming inference.
Proficiency in Python (required), Java, or similar, with software engineering best practices for ML workflows.
Experience with unit, integration, and pipeline-level testing for ML models, including data validation, correctness checks, and reproducibility.
Familiarity with cloud platforms (AWS preferred: SageMaker, S3, EC2) and reproducible ML pipelines.
Experience with CI/CD, Orchestration tools (Airflow, MLflow, Kubernetes, Terraform) and ML/data platforms(SageMaker, Databricks, Unity Catalog, Snowflake/Snowpark) to build scalable ML data pipelines and model workflows.
Strong collaboration skills to work effectively with AI scientists, software engineers, and regulatory teams.

Nice To Haves

Prior experience with Google Workspace (GMail, Drive, Docs, Sheets, Slides), Slack, Confluence/Jira, and Zoom Video is a plus.

Responsibilities

Collaborate with AI scientists to package and deploy ML models, ensuring reproducibility, versioning, and compliance.
Build and maintain model serving infrastructure including monitoring, drift detection, automated retraining, and logging.
Implement unit, integration, and system-level testing for ML models, covering data validation, model correctness, and deployment workflows.
Develop and operate end-to-end ML pipelines: ingestion → preprocessing → feature engineering → evaluation → deployment → monitoring.
Integrate CI/CD and MLOps practices for automated model builds, testing, and deployment.
Identify and resolve workflow inefficiencies or gaps between research and production.
Recommend and integrate frameworks, libraries, and infrastructure to improve pipeline efficiency, maintainability, and observability.
Collaborate cross-functionally to ensure compliance with regulatory requirements (FDA/HIPAA) in production ML workflows.