Software Architect
Machinify
| Company | Machinify |
| Category | Engineering |
| Location | Remote - US |
| Remote | Remote |
| Employment | Not stated |
| Level | Lead |
| Salary | Not stated by the employer |
| Posted | 17 Apr 2026 |
| Last verified | 31 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering unmatched value, transparency, and efficiency to health plan clients across the country. Deployed by over 85 health plans, including many of the top 20, and representing more than 270 million lives, Machinify brings together a fully configurable and content-rich, AI-powered platform along with best-in-class expertise. We’re constantly reimagining what’s possible in our industry, creating disruptively simple, powerfully clear ways to maximize financial outcomes and drive down healthcare costs. JD for Software Architect:
Software Architect | Intelligent Document Processing & Agentic Systems
Location: Remote - US
About Machinify
Machinify is a healthcare intelligence platform deployed across 85+ health plans, serving over 270 million lives. We build AI-powered systems for payment integrity and audit solutions that optimize financial outcomes and reduce healthcare costs — processing hundreds of millions of documents and validating millions of claims annually.
The Role
We are seeking a Software Architect to own the design and implementation of our intelligent document processing and agentic evaluation framework. This is a hands-on, Senior Staff-level technical leadership role responsible for setting the architectural direction for how our platform ingests, understands, and reasons over complex healthcare documents at scale.
This role requires both architectural vision and strong implementation skills. You will design the APIs, abstractions, and frameworks that power agentic case analysis — and build them alongside a team of experienced engineers. The ideal candidate brings deep expertise in system design, has strong convictions about where abstraction boundaries belong, and writes Python that sets the standard for the team.
What You'll Do
Own the architecture of Machinify's intelligent document processing platform and agentic evaluation framework — from API design to implementation
Design abstractions and interfaces that enable agentic workflows to reason over medical records, claims data, and unstructured healthcare documents
Define the boundaries between orchestration, retrieval, LLM interaction, and domain logic — creating a framework that is principled yet pragmatic. Ensure the system scales.
Write performant production code — this is a hands-on role where you lead through implementation, not solely through design documents.
Make foundational technology decisions around prompt architecture, multi-modal LLM integration, RAG patterns, and workflow orchestration
Drive reliability and observability into AI systems that must operate at healthcare-grade standards
Collaborate closely with Data Science, Data Engineering, and Product teams to translate complex domain requirements into clean system design
Reduce technical debt and establish architectural patterns that scale with the platform
Mentor engineers on system design, API design, and building production-grade AI systems
What We're Looking For
10+ years of software engineering experience with a proven track record of owning system architecture at scale
Experience with Python & Scala/Java — deep experience designing Python-based frameworks and writing idiomatic, well-structured APIs in Python and Scala/Java.
Demonstrated ability to design effective abstractions: knowing when to generalize, when to keep things concrete, and where to draw boundaries in complex systems
Experience architecting agentic or multi-step reasoning systems that integrate LLMs into production workflows — framework-agnostic design thinking is valued over familiarity with any specific tool
Strong background in designing large-scale systems that process unstructured data such as documents, medical records, and images
Deep understanding of API design principles and building platforms that oth
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