Senior Machine Learning Software Engineer

Cleerly is a healthcare company focused on transforming heart disease diagnosis and treatment through innovative technology. They are looking for a senior machine learning software engineer to design, build, deploy, and optimize ML services in a regulated healthcare environment, collaborating with AI scientists and ensuring compliance with regulatory requirements.

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

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