Staff AI Engineer

R1 RCM

. Design, build, and iterate on agentic AI systems for complex healthcare workflows, including documentation, coding, denial management, appeals, and revenue cycle automation .

Posted 10/7/2026full-timeNew York City • New York • United StatesLead💰 $243,915 - $470,408 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in designing and building agentic AI systems for healthcare workflows, with a strong focus on performance measurement, evaluation, and production system reliability. Proficient in Python and capable of leading cross-functional teams to turn complex workflows into structured AI solutions.

Highest-signal resume keywords

12+ Years Of Software Engineering ExperienceProficient In PythonExperience With APIs And Structured DataBuilding Evaluation Systems For AIOrganizational-Level Impact And Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringMachine Learning EngineeringApplied AI ExperienceProduction Systems DevelopmentAI System EvaluationClassificationRankingExtractionDecisioningLLM Applications

Soft Skills

Organizational LeadershipCollaboration With ExpertsProblem FramingDebugging Real-World Failures

Tools & Technologies

APIsAsync WorkflowsTestingLoggingObservabilityAI Infrastructure

Industry Keywords

Healthcare WorkflowsAgentic AI SystemsEvaluation SystemsFeedback LoopsHuman-In-The-Loop Review

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Design, build, and iterate on agentic AI systems for complex healthcare workflows, including documentation, coding, denial management, appeals, and revenue cycle automation
Develop long-horizon agent behavior across context construction, retrieval, tool use, memory, routing, verification, escalation, and human-in-the-loop review
Define end-to-end success criteria for clinical agents through specifications, rubrics, gold standards, test cases, and clinically meaningful measures
Build evaluation and feedback loops using expert review, production logs, model outputs, and benchmarks
Measure performance, regressions, edge cases, safety, reliability, provenance quality, and business impact
Prototype AI capabilities and harden them into reliable, explainable, auditable production systems
Partner with research and ML engineering teams on model selection, fine-tuning, reward modeling, distillation, synthetic data, post-training, and AI infrastructure
Own the process from problem framing through agent design, evaluation, deployment, trace analysis, and ongoing improvement

Requirements

What you’ll need

12+ years of software engineering, ML engineering, research engineering, or applied AI experience
Organizational-level impact and experience leading multiple teams or broad initiatives simultaneously
Highly proficient in Python
Experience building production systems with APIs, structured data, async workflows, testing, logging, and observability
Experience turning messy real-world workflows into structured AI problems, including classification, ranking, extraction, decisioning, LLM applications, agents, RAG, tool calling, structured outputs, prompting, or evaluation
Experience building or operating evaluation systems, benchmarks, annotation workflows, experiment tracking, or regression tests for AI systems
Ability to work with experts, debug real-world failures, and build reliable, correct, safe systems

Benefits

Comp & perks

Annual bonus plan at a target of 25.00%
Competitive benefits package
Opportunities to constantly learn, collaborate across groups and explore new career paths
Opportunity to contribute, think boldly and create meaningful work
Reasonable accommodation support for applicants with disabilities