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.
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.
Compensation
The base salary range for this role varies by location and is aligned to market benchmarks. 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.
This role is eligible for a 15% target annual bonus, resulting in the following base salary and Total Target Compensation (TTC) ranges:
Base Salary: $153,000 - $179,000 TTC: $176,000 - $206,000