​​​​​​Full-Stack Engineer
Dallas, TX | On-site | Full-time
Reports to: Co-Founder & Chief Technology Officer (CTO)
Dotted-line reporting to: Vice President of Engineering (VP of Engineering) 

Why this role exists

Our client is an AI-powered healthcare technology company helping hospitals identify purchased-services overcharges before payment. The platform analyzes complex contracts and invoices to surface defensible, line-level findings and recommendations for accounts-payable teams—helping health systems catch costly discrepancies before dollars go out the door. The company is expanding its customer base and evolving its product from an early-stage MVP into a reliable, scalable enterprise platform. 

About the Role

This is an early, high-ownership engineering role responsible for the product surface analysts use and the system seams that feed it. You will ship across a production TypeScript stack, trace defects across the app, sync, and validation pipeline, and improve the reliability and performance of a multi-tenant enterprise product.

The first priority is strong full-stack engineering: delivering complete features, protecting data meaning, and owning what reaches production. You will also work with SpendRule’s agent layer and AI-assisted development environment, applying the same verification standard to generated output as to any other code.

What You’ll Own

Ship analyst-facing features end to end, from Prisma/Postgres models and migrations through Express routes, typed clients, and React/Next.js experiences you verify in the browser.

Build for incomplete and delayed data. Treat loading, empty, partial, and error states as part of the feature, and preserve the difference between unknown, zero, verdict, and recommendation.

Diagnose issues at the seams between the app, sync jobs, and validation pipeline. Use queries, logs, traces, tests, and reproduction evidence to find the real failure point.

Improve performance and reliability across large data grids, queries, routes, tenant-scoped workflows, auth gates, rate limits, audit logging, and internal administration tools.

Use AI coding agents deliberately: provide precise plans, review generated diffs line by line, validate edge cases, and retain full ownership of the result.

Close work with an honest evidence trail: root cause, what changed, how it was verified, what remains untested, and which follow-up risks still exist.

What Success Looks Like

First 30 Days

Understand the path from contract and invoice ingestion through validation, sync, and the analyst-facing application.

Run the product and test suite, learn tenant and data-integrity boundaries, and map the highest-risk portal seams and performance constraints.

Deliver an initial production-ready improvement with clear verification evidence and appropriate code-review support.

By 60 Days

Ship at least one meaningful customer-facing feature or improvement across the full stack.

Diagnose and resolve an issue that crosses application or system boundaries.

Demonstrate effective use of the team's AI development tools while independently reviewing and validating their output.

Contribute improvements to reliability, performance, maintainability, or developer workflow.

By 90 Days

Independently own and ship a meaningful full-stack portal feature, including partial and failure states, tests, rollout, and monitoring.

Diagnose and improve at least one material latency, reliability, or data-integrity issue across multiple system layers.

Take dependable ownership of portal work so current technical leaders can return more capacity to core back-end product priorities.

Demonstrate disciplined use of AI-assisted engineering through clear plans, rigorous review, and evidence-backed verification.

Qualifications

5+ years shipping production TypeScript across both sides of the wire, including React or Next.js and a Node back end with a relational schema.

Strong SQL fundamentals and practical Postgres experience, including joins, indexes, query plans, and performance diagnosis.

Evidence of owning customer-facing features through design, implementation, testing, rollout, and production follow-through.

Experience debugging across multiple services or layers and measuring before-and-after results instead of relying on intuition.

Experience using AI coding agents on real work, with concrete examples of directing, reviewing, verifying, and correcting their output.

Experience building operational interfaces such as tables, filters, detail views, document viewers, exports, or other dense workflows, with attention to accessibility and incomplete states.

Ability to operate from incomplete specifications, ask focused questions, make appropriate decisions, and communicate technical findings clearly to non-technical stakeholders.

Strong Preferences

Experience with Prisma, Express, Next.js App Router, multi-tenant SaaS, enterprise auth, Redis, S3, or comparable technologies.

Performance work on large grids, high-volume queries, or sync-driven applications with measured results.

Experience in healthcare, procurement, AP automation, fintech, audit-heavy workflows, or another domain where data claims must be defensible.

Nice to Have

Python or FastAPI exposure and the ability to read adjacent pipeline code.

Experience with RAG, vector search, LLM evaluations, tool-calling agents, or customer-facing AI output.

Experience with SAML/OIDC, SCIM, tenant provisioning, or enterprise access controls.

Compensation & Benefits

• Competitive compensation
• Significant ownership and influence as part of a small engineering team
• Opportunity to work at the intersection of applied AI, enterprise software, and healthcare financial operations