Principal ML Engineer: Scoring, Production ML & GenAI

About Medical Guardian:

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. With 13 consecutive years on the Inc. 5000 list of Fastest Growing Companies,we’reredefining what it means to age confidently and independently.

We support over
625,000 members nationwide with life-saving emergency response systems and remote patient monitoring solutions. 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.

We are looking for aPrincipal Machine Learning Engineer to serve as a hands-on technical leader for machine learning, predictive modeling, scoring, decisioning, and applied AI initiatives. This role will primarily focus on building,validating, deploying, and improving machine learning models, while also bringing principal-level judgment to problem definition, model design, stakeholder engagement, and production readiness.

This is ahands-on model-building role first. 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. They should also be able tooperatewith the maturity of a principal-level engineer: shaping unclear problems, making pragmatic technical decisions, mentoring others, and driving work forward without waiting for perfect requirements.

• Build, test,validate, and improve machine learning models for scoring, prediction, prioritization, risk detection, engagement, intervention targeting, and decision support.
• 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.
• Use tools such as Python, SQL, Spark, Databricks,MLflow, scikit-learn,XGBoost, or similar platforms and libraries.
• Move quickly from data exploration to prototype tovalidatedmodel to production-ready capability.

• 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.
• Evaluate models for accuracy, calibration, stability, drift, fairness, interpretability, and operational usefulness.
• Help stakeholders understand what a score representations, how it should be used, how it should not be used, and how changes in the score should be interpreted.
• Document model logic, features, assumptions, limitations, validation results, and recommended usage in a way that business and technical stakeholders can understand.
• Define the evidence needed to show that a model or score is valid, stable, explainable, actionable, and useful.

• 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.
• Help define data pipelines, feature pipelines, inference flows, model outputs, feedback loops, and monitoring requirements.
• Ensure models are observable, supportable, secure, scalable, and aligned with enterprise architecture and governance expectations.
• Establish practical monitoring and feedback loops todeterminewhether models continue to perform and create value over time.

• Operate effectively in a rapid-build, startup-like environment where speed, ownership, and pragmatic decision-making are important.
• Turn early-stage ideas, ambiguous business needs, and rough concepts into working ML products, scores, prototypes, and production capabilities.
• Bring a product-engineering mindset to ML development, including user needs, workflow integration, adoption, usability, feedback loops, and measurable outcomes.
• Drive work forward without waiting for perfect requirements, while stillidentifyingcritical assumptions, risks, dependencies, and evidence needed before scaling.
• Partner with business and product stakeholders to define MVPs, iterate quickly, learn from usage, and improve models over time.
• Make smart tradeoffs between quick prototypes, durable platforms, transparent models, GenAI-enabled…