AI Evaluation Engineer

About Judi Health

Judi Health is an enterprise health technology company providing a comprehensive suite of solutions for employers and health plans, including:

Judi Rx , a public benefit corporation delivering full-service pharmacy benefit management (PBM) solutions to self-insured employers,

Judi Health™ , which offers full-service health benefit management solutions to employers, TPAs, and health plans, and

Judi® , the industry’s leading proprietary Enterprise Health Platform (EHP), which consolidates all claim administration-related workflows in one scalable, secure platform.

Together with our clients, we’re rebuilding trust in healthcare in the U.S. and deploying the infrastructure we need for the care we deserve. To learn more, visit www.judi.health .

Position Summary

As an AI Evaluation Engineer at Judi Health, you will build the testing frameworks, metrics, and tooling used to assess the safety, reliability, and accuracy of AI models and autonomous agents in production. This role bridges the gap between model development and real‑world usage by translating ambiguous product goals into measurable quality targets.

We’re looking for someone to lead evaluation end-to-end — from unit and integration testing to offline, online, and statistical evaluations of probabilistic systems. What we need is someone who can design and operate robust evaluation frameworks, partner with scientists and engineers, and ensure we can confidently answer questions like: “Did this change improve or degrade quality, safety, or user outcomes?”

What You’ll Build

Evaluation & Quality Pipelines

Build data evaluation pipelines that collect production conversations and agent interactions

Reconstruct full sessions from traces, logs, recordings, and transcripts

Apply labeling and scoring using human feedback signals (surveys, sentiment, outcomes) and automated evaluators (e.g., LLM‑as‑judge)

Continuous Quality & Safety Benchmarking

Own weekly and on‑demand automated evaluation runs against staging and production

Define benchmarks that track accuracy, reliability, and safety‑related signals

Produce trend dashboards that clearly answer: “Did this deploy change quality or risk?”

  Unified Evaluation Framework

Design and extend a standardized evaluation framework that supports multiple agent types and workflows

Translate high‑level product expectations into concrete success criteria and metrics

Ensure new agents and features can be evaluated consistently with minimal friction

Self Service Evaluation Tooling

Build APIs and internal tools so data scientists and engineers can go from “interesting scenario” to “included in the eval suite” quickly

Enable scenario curation, dataset management, and eval execution without deep infrastructure knowledge

  Experiment Tracking & Visibility

Provide shared visibility into prompt, model, and agent experiments

Enable reproducibility and comparison across runs so teams can build on each other’s work instead of operating in silos

Position Responsibilities:

Data Engineering

Build and maintain ETL pipelines for heterogeneous data sources (traces, logs, transcripts, user feedback)

Implement complex data stitching and session reconstruction logic

Manage dataset versioning, provenance, and lifecycle

Platform & Observability

Develop dashboards and monitoring tools for AI quality metrics

Integrate evaluations into CI/CD pipelines for scheduled and gated runs

Implement alerting on quality and safety signals, not just infrastructure health

AI / ML Evaluation Tooling

Apply and extend LLM‑as‑judge evaluation patterns

Design metrics and scoring approaches suitable for stochastic, non‑deterministic systems

Use tools like LangSmith to track runs, traces, experiments, and evaluation results

Collaboration

Partner closely with data science, engineering, and product teams

Translate between research goals, product intent, and engineering constraints

Help define what “good” looks like for AI behavior in production

Advocate for strong developer experience and usability in the tools you build

Responsible for adherence to the Capital Rx Code of Conduct including the reporting of non-compliance.

Required Qualifications

4+ years of experience in data engineering, ML engineering, or software engineering

Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related quantitative field • Strong proficiency in Python • Experience building and maintaining production data pipelines • Strong SQL skills • Experience working with at least one cloud platform (AWS preferred)

Nice ‑ to ‑ Haves

Prior work on LLM or agent evaluation infrastructure

Familiarity with designing metrics for safety, reliability, or quality in AI systems

Experience with voice or call‑center data (audio, transcripts, sentiment)

Experience with browser automation tools (e.g., Playwright) for end‑to‑end evals

Deep SQL expertise

New York, NY Salary Range $161,600 — $200,000 USD

Denver, CO Salary Range $148,400 — $185,000 USD

Charlotte, NC Salary Range $134,800 — $168,500 USD

All employees are responsible for adherence to the Judi Health Code of Conduct including the reporting of non-compliance. This position description is designed to be flexible, allowing management the opportunity to assign or reassign duties and responsibilities as needed to best meet organizational goals.

We provide equal employment opportunities to all employees and applicants for employment and prohibit discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, medical condition, genetic information, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.  

By submitting an application, you agree to the retention of your personal data for consideration for a future position at Judi Health. More details about Judi Health's privacy practices can be found at   https://www.judi.health/legal/privacy-policy .