Senior ML​/AI Engineer

About Care Scout

Join us on a mission to simplify and dignify the aging experience. We are the children, siblings, neighbors, and friends of those navigating the fragmented and confusing system of long‑term care. Our team is ferociously curious and relentless in our pursuit of a better system – and we are deeply committed to a sense of belonging for all, in all phases of life.

Senior ML/AI Engineer

This position is available to candidates in Richmond, VA (Hybrid) or remote applicants residing in states/locations under Eastern Standard Time:
Connecticut, Delaware, Florida, Georgia, Indiana, Kentucky, Maine, Maryland, Massachusetts, Michigan, New Hampshire, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Rhode Island, South Carolina, Tennessee, Vermont, Virginia, Washington DC, or West Virginia.

• This role is not eligible for employment visa sponsorship *

We are seeking a highly skilled and experienced Senior AI/ML Engineer to join our growing data and machine learning organization. In this role, you will design, build, and scale intelligent systems that power our product, operations, and analytics. You will work closely with data engineers, product managers, platform engineers, and business stakeholders to develop production‑grade machine learning models and AI‑driven solutions on top of our Databricks Lakehouse platform.

A successful candidate is both an innovative ML practitioner and a strong hands‑on engineer who can take projects from concept to production. You are comfortable navigating ambiguity, working with incomplete data, leading technical discussions, and implementing systems that are robust, observable, and maintainable. You thrive in collaborative environments and enjoy building scalable ML foundations that accelerate development across teams.

• Build, train, evaluate, and deploy machine learning models for prediction, classification, NLP, anomaly detection, and generative AI use cases.
• Apply modern ML techniques, experimentation frameworks, and statistical best practices to ensure model accuracy, fairness, and reliability.
• Develop LLM‑driven applications, prompt engineering strategies, and retrieval‑augmented generation (RAG) systems when applicable.

• Design and implement scalable features using Delta Lake, Spark, and Databricks Feature Store.
• Partner with data engineering teams to understand data availability, quality, lineage, and ingestion patterns.
• Build automated, reproducible pipelines that support training, validation, and model refresh cycles.

• Own end‑to‑end ML lifecycle using Databricks workflows, MLflow, feature stores, and model registries.
• Develop CI/CD and automated model deployment pipelines that ensure performance and reliability.
• Implement monitoring for drift, model degradation, data quality, and performance regressions.

• Design modular, scalable ML architectures that integrate with APIs, data warehouses, microservices, and downstream applications.
• Evaluate when to apply classical ML, deep learning, or LLM‑driven approaches based on business constraints.

• Develop A/B tests, offline/online evaluation frameworks, and statistical validation strategies.
• Analyze model results with clarity and communicate insights to technical and non‑technical partners.

• Work closely with product, engineering, and business teams to identify ML opportunities, refine requirements, and deliver measurable outcomes.
• Participate in architecture reviews, technical planning sessions, and roadmap discussions.
• Document work in a way that is scalable and easy for future engineers to adopt.

• Stay up to date on emerging ML frameworks, LLM advancements, Databricks capabilities, and scalable architecture patterns.
• Explore new tools, libraries, and platforms that can enhance model performance or development efficiency.

• 7+ years of experience in machine learning, applied AI, or similar engineering roles.
• Strong expertise building ML models…