Staff Machine Learning Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Design and uphold enterprise-level ML infrastructure, which encompasses model versioning, automated testing frameworks, containerization strategies, CI/CD pipelines, and extensive monitoring systems for model performance, data integrity, and drift detection.

• Lead the MLOps strategy and set standards throughout the organization.

• Provide mentorship to data scientists and engineers regarding production best practices, system architecture, and scalable design patterns.

• Manage the entire process from model development to production deployment, including real-time and batch inference systems, A/B testing frameworks, and automated retraining pipelines.

• Work alongside clinical leaders, product teams, and data scientists to convert intricate healthcare requirements into effective and scalable ML solutions.

• Communicate technical strategies to executive stakeholders.

• Create fault-tolerant, compliant systems that adhere to healthcare security and privacy regulations.

• Define SLAs, incident response protocols, and disaster recovery procedures for critical ML services.

• Assess and incorporate advanced MLOps tools and methodologies.

• Develop systems that can scale with Monogram's growth while minimizing operational overhead and enhancing model iteration speed.

⛳️ Requirements

• Bachelor’s degree in computer science, engineering, or a related field is required; a master’s degree is preferred.

• At least ten (10) years of experience in software engineering, with five (5) years concentrating on ML infrastructure, MLOps, or production ML systems, and Python development, alongside three (3) years of experience architecting and deploying production ML systems on cloud platforms (Azure preferred).

• A proven history of building and scaling ML platforms from inception.

• Experience in the healthcare or regulated industries is highly preferred.

• Expert-level knowledge of MLOps tools (MLflow, Kubeflow, SageMaker, Azure ML, etc.).

• Extensive experience with containerization (Docker, Kubernetes), orchestration tools (Airflow, Prefect), and infrastructure-as-code solutions (Terraform, ARM templates).

• Advanced understanding of CI/CD systems, automated testing methodologies, and GitOps workflows.

• Data engineering capabilities: SQL, Spark/PySpark, Databricks, and data pipeline optimization.

• Expertise in model monitoring, observability, feature stores, and experiment tracking at scale.

• Hands-on experience with both batch and real-time inference architectures.

• Familiarity with healthcare data standards (FHIR, HL7, claims data) is advantageous.

• Demonstrated ability to influence technical direction and guide senior engineers.

• Strong communication skills with the ability to simplify complex technical concepts for varied audiences.

• Proven track record of achieving consensus on architectural choices among multiple stakeholders.

• Systems thinking capabilities focusing on reliability, scalability, and maintainability are preferred.

• Knowledge of security, compliance, and privacy regulations in healthcare (HIPAA) is preferred.

• A proactive approach with a practical mindset towards technical debt and iterative enhancement is favored.

🏝️ Benefits

• Comprehensive Benefits - Medical, dental, and vision insurance, employee assistance program, employer-paid and voluntary life insurance, disability insurance, plus health and flexible spending accounts.

• Financial & Retirement Support – Competitive compensation, 401k with employer match, and financial wellness resources.

• Time Off & Leave – Paid holidays, flexible vacation time/PSSL, and paid parental leave.

• Wellness & Growth – Work-life assistance resources, physical wellness perks, mental health support, employee referral program, and BenefitHub for employee discounts.