Senior ML/AI Engineer

About TailorCare

TailorCare is transforming the experience of specialty care. Our comprehensive care program takes a deeply personal, evidence-based approach to improving patient outcomes for joint, back, and muscle conditions. By combining a careful assessment of patients' symptoms, health histories, preferences, and goals with predictive data and the latest evidence-based guidelines, we help patients choose and navigate the most effective treatment pathway for them, every step of the way.

TailorCare values the experiences and perspectives of individuals from all backgrounds. We are a highly collaborative, curious, and determined team passionate about scaling a high-growth start-up to improve the lives of those in pain. TailorCare is a remote-first company with a hybrid office in Nashville.

About the Role

TailorCare's Data Science team builds the ML/AI systems that decide which patients we reach, when, and how. We are shipping a portfolio of models and a real-time decision platform that puts them to work across every market we serve, and learns from every outcome it touches. The hard problem is not the score. It is which action, for whom, when, through which channel, and how the outcome feeds back. The engineer who builds that platform shapes how TailorCare delivers care at scale.

As a Senior ML/AI Engineer, you will take models from prototype to production and keep them running, and what you ship goes live with care teams working with real patients. You will own the engineering behind our predictive and decisioning systems: deployment and retraining workflows, monitoring, the real-time event-driven paths that connect model output to the teams and systems that act on it, and the GenAI applications our care teams already use. You will help grow our platform from a Databricks lakehouse toward a hybrid architecture on AWS, and you will raise the engineering bar for a team of strong modelers and healthcare data scientists. Reporting to the Director, AI & Decision Intelligence, you will work closely with data engineering, software engineering, clinical, and operations partners.

Primary Responsibilities

• Build the decisioning and feedback layer between score and action: action selection logic, holdout and experiment assignment, outcome capture, and the pipelines that measure lift per action and channel.
• Design and build event-driven, real-time integrations between model output and the systems that act on it: streaming pipelines, versioned schemas and contracts, replay, ordering, and failure handling.
• Own cost-to-serve across the platform: instrument compute, streaming, inference, and LLM spend per model and per decision, set budgets and alerts, and make cost a release criterion alongside latency and accuracy.
• Deploy, monitor, and retrain production ML models with CI/CD, model registry, and alerting that meet a healthcare reliability bar, including drift, coverage, and calibration by cohort and market.
• Build and improve predictive models on healthcare claims, authorization, and operational data, and validate them in production conditions before release.
• Drive the build-out of our AWS-native footprint alongside our Databricks lakehouse: infrastructure as code, IAM and secure data handling, containerized inference, and managed foundation-model services under PHI controls.
• Extend our feature store and data assets toward real-time features as new event sources arrive.
• Own and extend our production GenAI applications, including LLM pipelines and agentic workflows, with the evaluation, retrieval, guardrail, and cost controls needed to run them safely on patient data.
• Partner with data scientists to productionize new models, and with data and software engineering on shared pipelines, interfaces, and contracts.
• Help establish and uphold engineering standards with the ML engineering lead: code review, automated testing, reproducibility, documentation, and release discipline. Mentor data scientists on production practice.
• Other duties as assigned.

Minimum Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent experience.
• 5+ years in ML engineering, MLOps, or data science, including 3+ years operating ML systems in production.
• Production experience with Databricks, PySpark, Delta Lake, and MLflow.
• Production experience with AWS data and ML services and infrastructure as code (Terraform, CDK, or CloudFormation).
• Hands-on experience designing and running event-driven or streaming systems (Kinesis, Kafka, or equivalent) with schema or contract management.
• Strong Python and SQL, and experience building and evaluating ML models with scikit-learn, XGBoost, or PyTorch.
• CI/CD for ML workloads and model and pipeline monitoring.
• Ability and willingness to travel up to 10% as needed for onsite meetings, team collaboration, and company events.

Desired Qualifications

• Experience building recommendation, decisioning, or experimentation systems in production, including uplift or bandit-style approaches.
• dbt and lakehouse data modeling.
• Healthcare data (claims, eligibility, prior authorization, EHR) and PHI handling requirements.
• Containerized model serving and staged or canary deployment patterns.
• Amazon Bedrock or other managed foundation-model platforms under a HIPAA BAA.
• Agentic frameworks and retrieval systems in production.
• Experience building or operating LLM-based applications, including evaluation and guardrails.

Skills

• Engineering Mentality – Finds the common denominator across work and builds the platform that makes the next model faster to ship than the last.
• Scientific Approach – Starts from an open question, designs the analysis or model to answer it, and validates before trusting it.
• Seek Simplicity – Builds with the minimum necessary complexity and designs for the person who maintains it next.
• Ownership – Takes a system from design to production to monitoring without waiting for another team to carry it.
• Collaborative – Works across data engineering, software engineering, clinical, and operations to agree on interfaces and hold to them.
• Good Communication – Explains what a system does and why it matters in terms a care team or finance partner can act on.