Staff Machine Learning Engineer

Responsibilities

• check_circle Architect and maintain enterprise-grade ML infrastructure, including model versioning, automated testing frameworks, containerization strategies, CI/CD pipelines, and comprehensive monitoring systems for model performance, data quality, and drift detection.
• check_circle Drive MLOps strategy and standards across the organization. Mentor data scientists and engineers on production best practices, system design, and scalable architecture patterns.
• check_circle Own the complete journey from model development through production deployment, including real-time and batch inference systems, A/B testing frameworks, and automated retraining pipelines.
• check_circle Collaborate with clinical leaders, product teams, and data scientists to translate complex healthcare requirements into robust, scalable ML solutions. Present technical strategies to executive stakeholders.
• check_circle Build fault-tolerant, compliant systems that meet healthcare security and privacy standards. Establish SLAs, incident response protocols, and disaster recovery procedures for mission-critical ML services.
• check_circle Evaluate and integrate cutting-edge MLOps tools and practices. Design systems that scale with Monogram's growth while reducing operational overhead and improving model iteration velocity.
• check_circle Bachelor’s degree in computer science, engineering, or related field required; master’s degree preferred
• check_circle Minimum of ten (10) years in software engineering with five (5) years focused on ML infrastructure, MLOps, or production ML systems and Python development with strong software engineering fundamentals and three (3) years architecting and deploying production ML systems on cloud platforms (Azure preferred)
• check_circle Proven track record building and scaling ML platforms from the ground up
• check_circle Healthcare or regulated industry experience strongly preferred
• check_circle Expert-level proficiency with MLOps tooling (MLflow, Kubeflow, SageMaker, Azure ML, etc.)
• check_circle Deep experience with containerization (Docker, Kubernetes), orchestration tools (Airflow, Prefect), and infrastructure-as-code (Terraform, ARM templates)
• check_circle Advanced knowledge of CI/CD systems, automated testing strategies, and GitOps workflows
• check_circle Data engineering skills: SQL, Spark/PySpark, Databricks, data pipeline optimization
• check_circle Expertise in model monitoring, observability, feature stores, and experiment tracking at scale
• check_circle Production experience with both batch and real-time inference architectures
• check_circle Understanding of healthcare data standards (FHIR, HL7, claims data) is a plus
• check_circle Demonstrated ability to influence technical direction and mentor senior engineers
• check_circle Proven communication skills with ability to distill complex technical concepts for diverse audiences
• check_circle Track record of driving consensus on architectural decisions across multiple stakeholders
• check_circle Systems thinking skills with focus on reliability, scalability, and maintainability preferred
• check_circle Understanding of security, compliance, and privacy requirements in healthcare (HIPAA) preferred
• check_circle Bias toward action with pragmatic approach to technical debt and iterative improvement preferred

Benefits

• check_circle Comprehensive Benefits - Medical, dental, and vision insurance, employee assistance program, employer-paid and voluntary life insurance, disability insurance, plus health and flexible spending accounts
• check_circle Financial & Retirement Support – Competitive compensation, 401k with employer match, and financial wellness resources
• check_circle Time Off & Leave – Paid holidays, flexible vacation time/PSSL, and paid parental leave
• check_circle Wellness & Growth – Work life assistance resources, physical wellness perks, mental health support, employee referral program, and BenefitHub for employee discounts

About the company