Machine Learning Engineer

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Company Overview

A growing healthcare technology organization is reimagining how people access care by bringing healthcare services directly to patients’ homes. The organization uses technology inspired by marketplace and last-mile delivery platforms to make care more accessible, particularly for individuals who face barriers to visiting traditional healthcare facilities.

The organization has supported more than 2 million patients across 22 states and completed more than 130,000 in-home visits. Its multidisciplinary team includes clinicians, technologists, and operators working together to improve healthcare accessibility through technology and innovative care delivery models.

The organization is seeking a Machine Learning Engineer to build and maintain production systems that train, deploy, monitor, retrain, and serve machine-learning models reliably.

The role sits at the intersection of software engineering, data engineering, and machine learning, with a focus on turning machine-learning models into dependable production systems. The successful candidate will build training and inference pipelines, serve predictions through APIs and batch jobs, and implement monitoring systems capable of identifying data drift, performance degradation, and other issues before they affect patients or partners.

Hybrid & Office Experience

The organization operates on a hybrid schedule, with employees working from the office Monday through Thursday and working from anywhere on Fridays.

The organization values work-life balance and provides flexibility when personal circumstances require it. Employees are encouraged to collaborate in person during the core office days while maintaining flexibility where appropriate.

The organization also provides lunch each day and encourages employees to spend time together outside of meetings to strengthen team collaboration and connection.

Key Responsibilities

Production ML Systems

• Build and strengthen machine-learning training pipelines.
• Package machine-learning models for reliable deployment.
• Serve predictions through APIs and batch jobs with a focus on reliability and scalability.
• Maintain feature pipelines and ensure that features remain accurate, fresh, and reliable.

Reliability & Observability

• Monitor model drift, data quality, latency, cost, and overall performance.
• Automate model retraining and validation processes.
• Design safe rollback mechanisms for machine-learning deployments.
• Prevent training-serving skew and identify silent model degradation.
• Build monitoring and observability systems that provide early detection of production issues.

Collaboration & Engineering Excellence

• Productionize models developed and handed off by other teams.
• Build clean interfaces between data, model, and product systems.
• Implement reproducibility and versioning across data, features, and models.
• Maintain appropriate model-governance artifacts and engineering practices.

Required Qualifications

• Strong Python programming and software-engineering fundamentals.
• Experience working with machine-learning frameworks, data pipelines, and model-serving systems.
• Demonstrated experience taking machine-learning models from prototype to reliable production environments.
• Experience with cloud infrastructure, containers, CI/CD, and orchestration technologies.
• Experience with monitoring and observability systems.
• Experience implementing reproducibility and versioning across data, features, and machine-learning models.
• Understanding of security and privacy controls for sensitive data.
• Ability to collaborate effectively across software engineering, data engineering, machine-learning, and product teams.

Preferred Qualifications

• Background in backend engineering, data engineering, MLOps, or platform engineering.
• Experience working with feature stores or large-scale feature pipelines.
• Familiarity with healthcare data and systems designed to protect sensitive health information.
• Experience building production-grade machine-learning infrastructure.
• Strong understanding of model lifecycle management and production reliability.
• Experience designing scalable training and inference architectures.

Disclaimer

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