Engineer, Data & Integrations

A federal arbitration system called Independent Dispute Resolution, or IDR, now determines billions of dollars in healthcare payments each year. Providers win the vast majority of disputes, yet most eligible claims are never filed. The process is manual, fragmented, and resource-intensive, and most providers don’t have the infrastructure to pursue what they’re owed. The No Surprises Act created the framework, and the market already exists. Today it runs on spreadsheets, consultants, and static playbooks. We’re building the first intelligent system designed to operate inside it. Everything we do starts with data we don’t control. A health system sends us 835 remittance files, 837 claim files, and explanation-of-benefits documents. If we can’t reliably turn that into structured, trustworthy records, nothing downstream works.

Why This Is Hard (and Interesting)

Healthcare data is genuinely, memorably bad. An EOB arrives as a scanned PDF at fax resolution. Each payer formats theirs differently, so Cigna’s table layout has nothing in common with United’s. A single claim gets split across CPT codes in ways that only make sense if you know the payer’s internal logic. Some of our customers have six separate legacy systems that reports have to be pulled from, none of which have an API. Then there’s the standards layer. 835 and 837 files follow the EDI spec, mostly. The parts where they don’t are where the interesting bugs live. The prize for solving this is large. Reliable ingestion is the thing that lets everything else scale, and almost nobody in this market has done it well., You’ll own how data gets into Recourse and stays trustworthy once it’s there. You will:

• Build and maintain the ingestion pipeline for 835 and 837 files across every customer format we encounter
• Own document parsing: OCR on scanned EOBs, LLM-assisted extraction, and the consistency guarantees that make it reliable
• Build integrations with customer systems, SFTP endpoints, clearinghouses, and eligibility services
• Design the data model that everything downstream depends on
• Build the validation and reconciliation layer that catches bad data before it reaches a claim
• Solve the “same document, two different answers” problem, which is the hardest reliability issue we have
• Partner with operations to understand what real-world data actually looks like, not what the spec says it should look like

We build with Claude Code daily and run a Codex review pass on every slice. The stack is TypeScript, Next.js, Prisma, and PostgreSQL on Google Cloud, with LLM reasoning throughout.

Who You Are

You like the unglamorous problems. Parsers, pipelines, edge cases, and reconciliation. The stuff that never demos well but decides whether the company works. You are AI-pilled. You’ve used LLMs for real extraction and parsing work and you know where they help and where they hallucinate. You have opinions about which tools are useful and which are hype. You are rigorous about correctness. Determinism matters here. If a pipeline gives different answers on different runs, that’s a bug you take personally. You have a bias to action. You don’t default to no. Speed of iteration over polish of iteration. You start, you learn, you fix things in motion. Most decisions are reversible and do not need extensive study. You are intellectually honest. You ask questions when you don’t understand something. You say so when you’re wrong. You use plain language. You respectfully challenge decisions you disagree with, and once a decision is made, you commit. You put the team first. You are reliable and fully invested. You take your vacations. You check on your teammates. You help build a culture where people do their best work because they are supported, not squeezed.

Healthcare, Electronic Data Interchange (EDI), Spreadsheets, Adobe Acrobat, Litigation, Finance, Theater, Product Engineering, Sales Pipeline, Secure/SSH File Transfer Protocol (SFTP), * 4+ years building data pipelines, integrations, or backend systems in production

• Strong SQL and data modeling instincts
• Experience with messy real-world data: parsing, extraction, validation, reconciliation
• Comfort with TypeScript or Python. The specific stack matters less than the ability to pick things up
• Hands-on experience using LLMs for structured extraction from unstructured sources
• Experience building in regulated or security-sensitive environments (HIPAA, SOC 2, PCI, financial controls) is a plus
• A preference for small teams and early-stage chaos over mature org charts

Strong plus, not required: healthcare data specifically. EDI, X12, 835/837, HL7, FHIR, clearinghouses, or revenue cycle systems. If you have it, you’ll move faster. If you don’t, we’ll teach you. Sound judgment, technical depth, and ownership mindset are required. Grit matters more than pedigree.

Data quality is the constraint on this entire business. Solve it well and every other part of the company gets faster. That’s a rare position to be in as an engineer. You’ll also build expertise in a genuinely scarce area. Healthcare data engineering is unglamorous, hard, and in permanent demand. The people who are good at it never struggle to find interesting work. If you want this to be the most memorable stretch of your career, where you shipped something real, with a team you respected, in a domain that actually matters, this is the seat. Please apply even if you don’t fit 100% of these requirements. We would like to talk.

Recourse is being built in partnership with 25M Health, a healthtech venture firm. We have institutional backing, a shared platform team spanning engineering, strategy, design, and back-office, and early access to large provider systems. We are actively filing disputes for real customers, including a large multi-facility health system and a litigation-finance partner with hundreds of millions in claim value. This is a funded, validated opportunity with real customers and real data. We are a small, nimble team. We move quickly and we value clarity over theater. We want this to be the best work of your career. The stretch you look back on as the one where you shipped real things, with people who raised your game, on something that mattered. We care about clear thinking, high ownership, intellectual honesty, and direct communication. We believe operations, product, and engineering should operate as one pod, not three functions. We want the machines to do machine work, and the humans to do their best work.