Sr. Machine Learning Software Engineer (AI Healthcare)

TL;DR

Sr. Machine Learning Software Engineer (AI Healthcare): Design, build, deploy, monitor, and optimize production-ready ML services in regulated healthcare with an accent on MLOps, CI/CD, and compliance. Focus on developing end-to-end ML pipelines, model serving infrastructure, and ensuring reliability, performance, and regulatory adherence.

Location: Remote (US locations preferred; salary varies by US state/city like CA, WA, NY, NJ, CT, MA, DC, or other US areas). Access to offices in Denver CO, New York NY, Dallas TX, Lisbon PT. Occasional travel required for team meetings (once per month/quarter).

Base Salary: $153,000 - $179,000 (varies by US location); TTC: $176,000 - $206,000 (incl. 15% bonus).

Company

Healthcare company revolutionizing heart disease diagnosis, treatment, and tracking with AI-driven precision diagnostic solutions.

What you will do

• Collaborate with AI scientists to package, deploy, and version ML models ensuring reproducibility and compliance.
• Build and maintain model serving infrastructure with monitoring, drift detection, automated retraining, and logging.
• Implement unit, integration, and system-level testing for ML models including data validation and deployment workflows.
• Develop end-to-end ML pipelines from ingestion to monitoring.
• Integrate CI/CD and MLOps practices for automated builds, testing, and deployment.
• Resolve workflow inefficiencies between research and production and recommend tools for efficiency and observability.
• Ensure compliance with regulatory requirements like FDA/HIPAA in ML workflows.

Requirements

• 7+ years in software engineering for ML production or ML platform delivery
• Hands-on experience deploying ML models via APIs, batch, or streaming.
• Proficiency in Python (required), Java, or similar with ML best practices.
• Experience with unit/integration/pipeline testing for ML including data validation.
• Familiarity with cloud platforms (AWS preferred: SageMaker, S3, EC2).
• Experience with CI/CD, orchestration (Airflow, MLflow, Kubernetes, Terraform), ML platforms (SageMaker, Databricks).
• Strong collaboration with AI scientists, engineers, regulatory teams.

Culture & Benefits

• Core values: Humility, Excellence, Accountability, Remarkable, Teamwork (HEART).
• Mostly remote with office access; occasional travel for meetings.
• Stock options, paid benefits, employee perks as part of total rewards.
• Use of Google Workspace, Slack, Confluence/Jira, Zoom.
• Commitment to quality, regulatory compliance, and continuous improvement.