Staff Machine Learning Engineer

Position: Staff Engineer, Machine Learning Operations

The Staff Engineer, MachineLearningOperations will architect, own, and scale the machine learning infrastructure and deployment pipelines that power Monogram's operational and clinical initiatives. Operating with full autonomy, you'll establish MLOps excellence, mentor engineering teams, and drive strategic technical decisions that directly impact patient outcomes. This role requires a seasoned engineer who can balance innovation with production reliability while building systems that handle healthcare's most sensitive and complex data.

Responsibilities

• 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.
• Drive MLOps strategy and standards across the organization. Mentor data scientists and engineers on production best practices, system design, and scalable architecture patterns.
• 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.
• 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.
• 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.
• 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.

Position Requirements

• Bachelor’s degree in computer science, engineering, or related field required; master’s degree preferred
• 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)
• Proven track record building and scaling ML platforms from the ground up
• Healthcare or regulated industry experience strongly preferred
• Expert-level proficiency with MLOps tooling (MLflow, Kubeflow, SageMaker, Azure ML, etc.)
• Deep experience with containerization (Docker, Kubernetes), orchestration tools (Airflow, Prefect), and infrastructure-as-code (Terraform, ARM templates)
• Advanced knowledge of CI/CD systems, automated testing strategies, and GitOps workflows
• Data engineering skills: SQL, Spark/PySpark, Databricks, data pipeline optimization
• Expertise in model monitoring, observability, feature stores, and experiment tracking at scale
• Production experience with both batch and real-time inference architectures
• Understanding of healthcare data standards (FHIR, HL7, claims data) is a plus
• Demonstrated ability to influence technical direction and mentor senior engineers
• Proven communication skills with ability to distill complex technical concepts for diverse audiences
• Track record of driving consensus on architectural decisions across multiple stakeholders
• Systems thinking skills with focus on reliability, scalability, and maintainability preferred
• Understanding of security, compliance, and privacy requirements in healthcare (HIPAA) preferred
• Bias toward action with pragmatic approach to technical debt and iterative improvement preferred

Compensation

The base salary range for this role is $170,000 to $221,000 and reflects the range of experience and qualifications we may consider. Final compensation will be determined based on geographic location and job-related factors such as experience, skills, education, certifications, and internal compensation considerations. While a larger range is posted, most offers will typically fall within a narrower band based on these factors.

In addition to base salary, this role is eligible for additional compensation, including bonus opportunities and a comprehensive benefits package.

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 – 20 PTO days, 7 paid holidays, and 1 floating day to make your own, and paid parental leave
• Wellness & Growth – Work life assistance resources, physical wellness perks, mental health support, employee referral program, and BenefitHub for employee discounts

About Monogram Health

Monogram Health is a leading multispecialty provider of in-home, evidence-based care for the most complex of patients who have multiple chronic conditions. Monogram health takes a comprehensive and personalized approach to a person’s health, treating not only a disease, but all of the chronic conditions that are present - such as diabetes, hypertension, chronic kidney disease, heart failure, depression, COPD, and other metabolic disorders.

Monogram Health employs a robust clinical team, leveraging specialists across multiple disciplines including nephrology, cardiology, endocrinology, pulmonology, behavioral health, and palliative care to diagnose and treat health issues; review and prescribe medication; provide guidance, education, and counselling on a patient’s healthcare options; as well as assist with daily needs such as access to food, eating healthy, transportation, financial assistance, and more. Monogram Health is available 24 hours a day, 7 days a week, and on holidays, to support and treat patients in their home.

Monogram Health’s personalized and innovative treatment model is proven to dramatically improve patient outcomes and quality of life while reducing medical costs across the health care continuum.

Originally posted on Himalayas