Staff Software Engineer

Position Overview

We are looking for a Staff Software Engineer, RCM AI Automation & Platform to provide technical leadership and help shape the next generation of Experity’s RCM technology.

In this role, you will lead the design and evolution of platform capabilities spanning multiple RCM workflows , including backend services, APIs, integrations, event-driven and long-running workflows, configurable business rules, data, and AI-enabled automation supporting charge entry, claims processing, claim submission, rejection and denial resolution, payments, and other parts of the revenue cycle.

You will work closely with Product Managers, RCM Operations, Software Engineers, Architects, Data teams, and QA to translate complex healthcare business workflows into scalable, maintainable software solutions.

As a Staff Engineer, your impact will extend beyond the software you personally build. You will provide technical direction across initiatives, establish reusable architecture and engineering patterns, solve high-ambiguity and cross-system problems, mentor engineers, and help teams make sound technical tradeoffs as the RCM platform evolves.

Success means more than delivering features. You will help increase automation, reduce unnecessary manual work, improve claim outcomes, shorten processing time, and build an RCM platform that can continue to evolve.

You will be expected to understand the business workflow behind the software, identify opportunities for automation, and determine when deterministic software, configurable rules, data, machine learning, or AI agents are most appropriate for the problem.

What You’ll Do

Architecture & Technical Leadership

·       Define and evolve the technical architecture for RCM automation capabilities spanning multiple products, services, workflows, and integrations.

·       Lead technical design for complex, high-impact initiatives where requirements, system boundaries, or implementation approaches may initially be ambiguous.

·       Establish reusable patterns for workflow orchestration, state management, business rules, integrations, event processing, AI-enabled automation, observability, and failure recovery.

·       Identify where capabilities should become reusable platform services versus remaining workflow- or product-specific implementations.

·       Drive technical decisions across service boundaries and work with architects and engineering teams to ensure solutions fit Experity's broader platform architecture.

·       Evaluate architectural tradeoffs involving scalability, reliability, maintainability, security, cost, speed of delivery, and operational complexity.

·       Reduce unnecessary technical complexity and prevent one-off solutions from becoming long-term platform liabilities.

RCM Platform Engineering

·       Design, develop, test, deploy, and operate scalable production software supporting Experity’s Revenue Cycle Management workflows.

·       Build backend services and REST APIs using TypeScript, Node.js, and cloud-native technologies.

·       Contribute to modern web applications when needed using technologies such as React, Next.js, and TypeScript.

·       Design and implement event-driven, queue-based, and asynchronous workflows with explicit state management, retries, redrive, exception handling, idempotency, and auditability.

·       Design long-running RCM workflows that support state transitions, human-in-the-loop processing, exception handling, replay/recovery, workflow versioning, and compensation or reversal when appropriate.

·       Ensure workflow designs can evolve across multiple RCM use cases without creating tightly coupled or duplicated implementations.

·       Build integrations with Experity applications, healthcare clearinghouses, payer systems, and third-party RCM services.

·       Design reliable interfaces for claims, payment, eligibility, rejection, denial, and other healthcare transaction workflows.

·       Define integration patterns and contracts that improve consistency, resilience, traceability, and reuse across RCM systems.

Rules, Data & Automation

·       Develop configurable business rules and workflow capabilities that replace logic currently distributed across application code, spreadsheets, and operational processes.

·       Build rules and workflow capabilities that are configurable, versioned, explainable, auditable, and safe to change without introducing unnecessary application dependencies.

·       Guide the architecture of reusable rules, workflow, and exception-management capabilities rather than embedding business logic independently within individual services.

·       Work with claims, financial, and operational data to improve automation, identify exceptions, and measure business outcomes.

AI & Agentic Engineering

·       Integrate Generative AI, LLMs, agents, retrieval, and machine learning capabilities where they provide measurable value, while using deterministic software and business rules where those approaches are more appropriate.

·       Build AI-enabled capabilities using clearly defined tools, APIs, contracts, permissions, validation, evaluation, observability, and fallback paths rather than treating LLMs as standalone solutions.

·       Define architectural patterns for safely incorporating AI agents into production RCM workflows, including tool boundaries, authorization, human-in-the-loop controls, deterministic guardrails, evaluation, fallback behavior, and auditability.

·       Help teams determine where AI adds measurable value and where conventional software, workflow orchestration, rules, or machine learning are more appropriate.

·       Drive reusable AI integration patterns so individual teams do not independently create incompatible agent, tool, retrieval, or evaluation architectures.

·       Use modern AI-assisted software development practices for codebase exploration, development, testing, debugging, documentation, and engineering productivity while maintaining appropriate engineering review and quality standards.

Reliability, Security & Operations

·       Build observability, automated testing, security, operational controls, and failure recovery into software from the beginning.

·       Design systems with healthcare security, PHI protection, least-privilege access, auditability, and safe operational logging in mind.

·       Define reliability and observability standards across RCM services and workflows, including traceability across asynchronous and distributed processing.

·       Lead technical investigation of complex production issues that cross application, integration, data, infrastructure, or workflow boundaries.

·       Identify systemic causes behind recurring incidents and drive platform-level improvements rather than repeatedly addressing symptoms.

Engineering Leadership & Influence

·       Serve as a technical leader and trusted engineering partner across RCM initiatives without requiring direct people-management responsibility.

·       Mentor engineers through architecture reviews, design discussions, code reviews, debugging, and complex technical decisions.

·       Raise engineering quality by establishing practical standards, reusable patterns, reference implementations, and technical guidance.

·       Influence engineering roadmaps by identifying foundational technical investments, architectural risks, scalability constraints, and modernization opportunities.

·       Partner with Product and RCM Operations to challenge assumptions, simplify workflows, and ensure technology decisions address the underlying business problem rather than simply automating existing processes.

·       Own critical software through production and continuously improve it based on system behavior, operational feedback, and business results.

·       Measure the impact of engineering improvements through outcomes such as automation rate, exception rate, manual touches, processing time, rejection/denial resolution time, successful resubmission, payment outcomes, and system reliability.

Qualifications

Required

·       8+ years of professional software engineering experience, with significant experience designing and operating complex production systems.

·       Demonstrated experience operating at a senior technical leadership level—leading architecture and technical direction across multiple engineers, services, or initiatives.

·       Strong software engineering fundamentals, including application design, testing, debugging, code review, and maintainability.

·       Deep experience building backend services, REST APIs, distributed applications, and transaction-processing systems.

·       Proficiency with TypeScript, Node.js, or comparable modern backend technologies.

·       Experience with relational databases, SQL, data modeling, and transactional systems.

·       Strong experience developing and operating cloud-hosted applications, preferably on AWS.

·       Deep understanding of asynchronous processing, queues, events, distributed-system failure modes, retries, idempotency, consistency, and resilient system design.

·       Experience architecting long-running workflow or orchestration systems involving state management, exceptions, retries, recovery, and human intervention.

·       Experience with automated testing, CI/CD pipelines, observability, and secure software development practices.

·       Ability to design systems and technical contracts that remain maintainable as teams, workflows, integrations, and transaction volumes grow.

·       Ability to reason across application, platform, integration, data, security, and operational boundaries rather than optimizing a single service in isolation.

·       Ability to investigate highly ambiguous problems using production behavior, data, logs, architecture, and user workflows.

·       Demonstrated ability to influence technical decisions across teams through technical credibility, communication, design artifacts, and collaboration rather than organizational authority.

·       Experience mentoring engineers and raising the technical capability of a broader engineering team.

·       Strong collaboration and communication skills with the ability to work across engineering, product, operations, architecture, data, and executive stakeholders.

·       Experience using modern AI-assisted software development tools and workflows while maintaining engineering quality, security, and accountability.

Preferred

Experience in one or more of the following areas is valuable:

·       Healthcare Revenue Cycle Management, medical billing, claims, rejections, denials, eligibility, remittance, payment processing, or claim remediation.

·       Healthcare claims transactions and standards such as 837, 835, CARC/RARC, or related payer/clearinghouse workflows.

·       Waystar or other healthcare clearinghouse integrations.

·       Healthcare, HealthTech, FinTech, insurance, payments, or other high-volume transactional environments.

·       Architecture of workflow orchestration, rules engines, state machines, or exception-management platforms used across multiple products or business processes.

·       Event-driven architectures and large-scale distributed systems.

·       Incremental modernization of legacy platforms while maintaining production continuity and avoiding high-risk rewrites.

·       Production use of Generative AI, LLMs, AI agents, retrieval-augmented generation, or machine learning.

·       AI orchestration frameworks, structured tool use, MCP or similar tool protocols, and agentic workflows.

·       Human-in-the-loop automation where AI or software recommendations require review, approval, correction, or escalation.

·       Architecture and governance of production AI/agent systems, including evaluation, guardrails, tool authorization, observability, and fallback strategies.

·       Snowflake or other cloud-native data platforms.

·       Docker, Kubernetes, and containerized application deployment.

·       Data-intensive application development and distributed data processing.

·       Experience working with HIPAA-regulated systems, PHI, audit requirements, or other highly regulated environments.

Technology Environment

Our RCM engineering environment includes:

·       Backend: TypeScript, Node.js, Python

·       Frontend: React, Next.js, TypeScript, Tailwind CSS

·       Cloud & Platform: AWS, Docker, Kubernetes, CI/CD

·       Architecture: REST APIs, queues, events, asynchronous workflows, distributed services

·       Data: SQL, Snowflake

·       AI: AWS Bedrock, LLM APIs, retrieval, structured tool use, Generative AI, and agentic workflows

·       Integrations: Waystar, Experity EMR and Practice Management, payer systems, and third-party RCM services

Candidates are not expected to have experience with every technology listed above. At the Staff level, depth of engineering judgment, experience solving complex cross-system problems, and ability to establish scalable technical patterns are more important than matching every technology in the stack.

How We Work

We want engineers who understand the problem they are solving, not just the ticket they are implementing.

At the Staff level, we expect you to look beyond an individual feature or service and understand how technical decisions affect the broader RCM platform.

You should be comfortable learning a complex healthcare domain, questioning existing processes, working through ambiguity, making practical technical tradeoffs, and taking ownership of difficult problems.

Our engineers collaborate closely with business and operational teams because understanding the real workflow is essential to building the right solution. We value pragmatic engineering, measurable outcomes, incremental modernization, and choosing the right technology for the problem rather than applying technology for its own sake.

Staff Engineers help turn repeated solutions into platform capabilities, repeated problems into engineering standards, and ambiguous business challenges into clear technical direction.

We expect engineers to understand the end-to-end RCM workflow, identify where AI and automation can eliminate unnecessary work, design for exceptions and failure from the beginning, and measure whether the software actually improves operational and financial outcomes.

We are building an AI-native engineering culture. Engineers are expected to use modern AI development tools thoughtfully to accelerate delivery and improve quality while retaining ownership of architecture, code quality, testing, security, and production outcomes.

Equal Opportunity Employer
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