Tech Lead

Why we started Trellis Before a patient can start a therapy, the prescription has to clear intake, a benefits check, an estimate of what the patient will owe, and prior authorization. The people doing that work are intake coordinators, benefits coordinators, and prior auth specialists. They sit in warehouses across the country, mostly in the Midwest, doing data entry. The therapies we work on are usually expensive enough that an insurer’s got to approve them first. Without that approval, the treatment might as well not exist. Jacky watched a parent wait a long time for approval on treatment that couldn’t wait. We met at the Stanford AI Lab, building foundational models for healthcare. Mac was leading AI and diagnostics work at the Stanford School of Medicine on patient identification. Jacky was a healthcare data scientist at the World Bank Group, doing econometrics in its development impact evaluation branch. Trellis didn’t start out on medication access. We started horizontal, automating document work for anyone with a paperwork problem, but our biggest customers were always healthcare companies. Technically that work isn’t so different from legal, but the nuance in healthcare runs deep enough that going the extra mile takes focus. At some point we were at a crossroads: double down and deliver a lot more value for our healthcare customers, or stay horizontal. We picked the first, because neither of us thinks science is what keeps people from being treated. What we’re building Getting one script from written to patient starting treatment takes about a hundred steps. Which hundred depends on the drug, the insurer, and which of a customer’s locations the script came from, and customers revise those procedures about once a quarter. We’re automating every step. Our agents do that work the way the people in those Midwest warehouses do, through the same screens. They read and write to electronic health records (EHRs) because those systems have no APIs worth using. They fill out insurance portals. Some of our agents aren’t in a browser at all: they drive desktop software directly. Some work the phones with payers, providers, and patients, and those calls get transcribed and summarized back into the record. Jacky’s shorthand for the end goal is “autopilot, not copilot.” A chatbot on a PDF document isn’t really helpful for this work. Today, autopilot looks like 10 minutes from a new referral to a submitted prior auth, 5 seconds for a benefits check, and 90% of scripts running end-to-end without a person touching them. We process ~30M scripts/year and $10B of therapies across 450+ locations in all 50 states. Some of those customers are Fortune 500 companies. Customer churn = zero. That last 10% is the cases a person still has to touch: a portal changed overnight or a location that runs its procedure differently. What we’re building toward is one motion: script in, patient ready for therapy. What you’ll own We went from 6 to 8 figures of revenue in less than a year. Jacky’s bar for this role is a question: what would someone who’s taken a company from zero to nine figures in two or three years want to change here? Engineering is 15 people today, in three pods: platform, deployed, and product. We want to 2x the size of the team by the end of the year. You’d set the coding bar, make the high-leverage technical calls on the team’s behalf including vendor selection, and own how engineers here write tests and ship. It’s a player/coach job; you’d still be writing code. The dev environment. Somewhere an engineer can pull any configuration down from prod into local. Jacky thinks a good one triples an engineer’s output. It’s where he’d want your first ninety days to go. What “as close to prod as it gets” means when prod is thousands of Windows sessions driving portals and EHRs we don’t own is yours to define. The fleet. Thousands of concurrent browser and desktop sessions run on Windows Server and Remote Desktop Services, around the clock. Keeping them up means:

• Diagnosing runaway processes, memory leaks, and zombie sessions on live hosts
• Handling loopback and local RDP architectures
• Shadowing sessions to debug an agent mid-run
• Maintaining golden images When a session host hangs or an agent stalls, it isn’t an abstract SLA breach; it’s a clinic waiting on a prescription. Self-healing. There are thousands of insurance portals, each with a different flow depending on the medication, and they change weekly or quarterly with no warning. We have some version of self-healing today: a failure escalates to someone on our ops team who fixes it by hand. But with thousands of portals, we want the system to do that itself. Memory. When a portal changes, an agent fails and somebody discovers it. When a payer policy, a clinical guideline, or a customer’s SOP changes, an agent can fill out a prior auth exactly the way it did last quarter and be wrong. Deciding what an agent should know, where that knowledge comes from, and how to cat