Medical Guardian

Medical Guardian

Principal Machine Learning Engineer

Philadelphia, Pennsylvania, United States · Principal

Sponsorship not specifiedDetected 63 days ago
PythonCode ReviewSQLSnowflakeDatabricksAWSAzurePlatform EngineeringMachine Learningscikit-learnSparkData AnalysisData EngineeringData ScienceLLMsRAGAgentic AIMLOpsA/B TestingLeadershipMentoringGeneralized Linear Models

About the role

  • We are looking for a Principal Machine Learning Engineer to serve as a hands-on technical leader for machine learning, predictive modeling, scoring, decisioning, and applied AI initiatives.
  • The ideal candidate should be comfortable spending most of their time working directly with data, features, models, scoring logic, validation methods, production workflows, and model improvement.

Responsibilities

  • Perform exploratory data analysis, data quality assessment, feature engineering, model training, model selection, and performance evaluation.
  • Develop practical ML models that balance predictive performance, explainability, stability, maintainability, and business usefulness.
  • Work with structured, semi-structured, and operational data to create model-ready datasets and reusable features.
  • Scoring, Scorecards, and Transparent Models Design and implement predictive scores, risk tiers, score bands, thresholds, cut points, and intervention logic.
  • Build transparent and interpretable models where explainability is important, including logistic regression, generalized linear models, decision trees, monotonic models, calibrated models, scorecard-style models, or explainable boosting approaches.
  • Document model logic, features, assumptions, limitations, validation results, and recommended usage in a way that business and technical stakeholders can understand.
  • Production ML and MLOps Partner with data engineering, analytics engineering, platform engineering, and application engineering teams to move models from experimentation into reliable production workflows.
  • Support model deployment, batch scoring, real-time or near-real-time inference, model versioning, monitoring, retraining, and performance tracking.

Requirements

  • 3+ years of experience deploying, operationalizing, or supporting models in production or business-critical environments.
  • Strong hands-on experience with Python and SQL.
  • Experience with modern ML and data platforms such as Databricks, Spark, MLflow, Snowflake, Azure, AWS, or similar technologies.
  • Strong understanding of model evaluation, calibration, thresholding, score interpretation, monitoring, drift, retraining, and production ML lifecycle management.
  • Ability to explain model behavior, model performance, assumptions, limitations, and tradeoffs to both technical and non-technical stakeholders.
  • Ability to work independently as a senior hands-on contributor while also providing technical leadership and modeling judgment.

Nice to have

  • 10+ years of relevant professional experience in ML, data science, applied AI, software engineering, decisioning systems, commercial software, or production analytics.
  • Experience with transparent or interpretable models such as logistic regression, GLMs, GAMs, decision trees, monotonic models, calibrated models, scorecard-based models, or Explainable Boosting Machines.
  • Experience designing score bands, thresholds, risk tiers, intervention rules, recommended actions, or decision logic based on model outputs.
  • Experience working in commercial software, SaaS, digital products, gaming, fintech, healthtech, consumer technology, marketplace, or other product-driven environments.
  • Experience partnering with product managers, designers, software engineers, business leaders, and operational teams to turn ML models into usable product capabilities.
  • Experience with GenAI, LLMs, RAG, AI agents, prompt engineering, model evaluation, conversational AI, summarization, document intelligence, or AI-enabled workflow automation.
  • Experience with MLOps practices including model registries, deployment pipelines, monitoring, drift detection, retraining strategies, and model governance.
  • Success in This Role Looks Like: High-quality models and scores are built, validated, deployed, monitored, and improved over time.

Benefits

  • Founded in 2005, Medical Guardian is a fast-growing digital health and safety company on a mission to help people live a life without limits.
  • Trusted by families, healthcare providers, and care managers, our work is powered by a culture of innovation, compassion, and purpose.
  • Medical Guardian boasts a 95% customer satisfaction rate, a #1 ranking on 16 medical alert consumer choice sites and achieves a 4.7+ star rating on Google Reviews.
  • Hands-On Model Development Build, test, validate, and improve machine learning models for scoring, prediction, prioritization, risk detection, engagement, intervention targeting, and decision support.
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