Sr. Machine Learning Software Engineer
Indexed description
Cleerly has created a new standard of care for heart disease through value-based, AI-driven precision diagnostic solutions with the goal of helping prevent heart attacks. Our technology goes beyond traditional measures of heart disease by enabling comprehensive quantification and characterization of atherosclerosis, or plaque buildup, in each of the heart arteries. Cleerly’s solutions are supported by more than a decade of performing some of the world’s largest clinical trials to identify important findings beyond symptoms that increase a person’s risk of heart attacks.
At Cleerly, we collaborate digitally and use a wide variety of systems. Our people use Google Workspace (GMail, Drive, Docs, Sheets, Slides), Slack, Confluence/Jira, and Zoom Video, prior experience in these areas is a plus. Role or department specific technology needs may vary and will be listed as requirements in the job description.
While we are mostly a remote company, travel is required for some team meetings and cross function projects typically once per month or once per quarter, for some roles like sales or external facing roles travel could be up to 90% of the time.
Cleerly is committed to providing safe and effective medical software that meets customer needs and our intended use. The adherence to all applicable regulatory and statutory requirements establishes a clear framework for setting measurable quality objectives. Our commitment to continually improving our products and processes proactively manages risks, ensuring ongoing compliance throughout the entire software lifecycle. Understanding this role's relevance and importance is critical to achieving Cleerly's quality objectives.
About The Opportunity
We are seeking a senior machine learning software engineer to design, build, deploy, monitor, and optimize production-ready ML services in regulated healthcare. You will work hands-on to package, test, orchestrate, deploy, and maintain ML models, improve workflows, and implement CI/CD and automated testing to ensure reliability, performance, and faster delivery of business value.
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.
- 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.
- Candidates located in higher-cost labor markets, including California, Washington, New York, New Jersey, Connecticut, Massachusetts, and Washington, DC represent the middle to high end of the range, while candidates located in all other U.S. locations represent the low to middle end of the range.
- Final compensation is determined based on location, experience, skills, and internal equity.
- Base Salary: $153,000 - $179,000
- TTC: $176,000 - $206,000
- Total Target Compensation (TTC): Total Cash Compensation (including base pay, variable pay, commission, bonuses, etc.) Additionally, stock options, paid benefits, and employee perks are part of your total rewards.
- H: Humility- be a servant leader
- E: Excellence- deliver world-changing results
- A: Accountability- do what you say; expect the same from others
- R: Remarkable- inspire & innovate with impact
- T: Teamwork- together we win
#Cleerly
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