# What the Abridge engineering interview is really like

Source: https://www.techinterview.org/post/3233477138/abridge-engineering-interview/
Updated: 2026-08-02 · techinterview.org

Abridge crossed $100M in annual recurring revenue in 2025 and raised at a $5.3B valuation, but the thing that actually shapes its engineering interview is narrower than any headline number: the product writes a clinical note that a doctor reviews, signs, and drops into your medical record. Get a medication or a dosage wrong there and it's a patient-safety problem, not a bug ticket. The loop is built to find people who feel the weight of that.

The company makes ambient clinical documentation. A clinician turns it on at the start of a visit, it listens to the conversation, and it produces a structured note that flows into Epic. Shiv Rao, a cardiologist at UPMC, and Zachary Lipton, formerly an ML professor at Carnegie Mellon, started it in 2018. That founding pair tells you what to expect: real ML depth sitting next to real clinical stakes. The customers are Kaiser Permanente, Mayo Clinic, Johns Hopkins, Duke, and around ninety other health systems, and the deployment target is Epic, an EHR with strict privacy rules and its own way of doing everything. That tension shows up in almost every round.

## The shape of the loop

Abridge runs a fairly standard late-stage-startup process, tuned toward practical engineering rather than puzzle-solving. Expect a recruiter call, a technical screen, and then an onsite (usually virtual) of three to four rounds. The exact mix depends on whether you're interviewing for backend, ML, full-stack, or infra, but the spine is consistent.

| Round | Format | What Abridge is testing |
| --- | --- | --- |
| Recruiter screen (~30 min) | Phone call | Background, level calibration, and why you want to work in clinical AI specifically |
| Technical screen (~60 min) | Live coding in a shared editor | Data structures and practical problem-solving; tries and string handling have come up |
| System or ML design (~60 min) | Discussion, virtual whiteboard | Designing a real-time transcription-to-note pipeline, or evaluating generative output, depending on the role |
| Craft round (~60 min) | Role-specific coding or a close look at past projects | Depth in your track: backend, infra, full-stack, or applied ML |
| Values and leadership (~45-60 min) | Behavioral conversation | Ownership, humility, and judgment when a clinical note comes out wrong |

Response times are quick by industry standards. Candidates report hearing back within about a week at each stage, and the recruiting team tends to keep you informed rather than going dark. That pace matters, because Abridge is hiring against other well-funded AI companies and the strong candidates have options.

## The recruiter screen

The first call is short and mostly about fit and calibration. Where you are in your search, what you're looking for, why healthcare. That last question is not a throwaway. Abridge screens hard for people who want to be in this domain specifically, because the work means reading about clinical workflows, sitting with the reality that your output goes into someone's chart, and caring when a clinician says the note missed something. If your real answer is "I just want to work on cool models," this probably is not your best fit, and they can tell.

## The coding round

The technical screen is live coding, usually one meaty problem with follow-ups rather than a stack of speed puzzles. Data structures come up for real. Candidates have been asked to implement a trie from scratch and then extend it, which fits a company that spends its life matching text against medical vocabularies. Expect the interviewer to push on edge cases and complexity, and to care more about how you reason than whether you land the textbook-optimal answer on the first pass.

Some problems recent candidates describe:

- "Implement a trie, then add prefix search and wildcard matching."

- "You have a stream of transcript tokens with duplicates from overlapping audio segments. Deduplicate within a sliding window."

- "Parse a messy timestamped transcript into clean speaker turns."

The through-line is that these look like the actual problems Abridge solves, not abstract riddles. Talk through your assumptions out loud, and write code you'd be willing to put in a review. Code that's clean and tested beats clever.

## System design, tuned to streaming and PHI

For most engineering roles there's a design round, and the prompts stay close to the product. A common one: design the pipeline that turns a live doctor-patient conversation into a finished note inside Epic. From there the interviewer pulls threads.

Where does speech recognition run, and is it streaming or batch? A 45-minute visit produces a lot of audio, so how do you keep memory and latency sane? Where does protected health information live at rest, and how do you keep it out of your logs? What happens when the model is unsure, and how does a clinician correct the note without retyping the whole thing? The best answers treat the clinician as the user and the note as the product, and they take failure modes seriously instead of waving past the messy parts.

If you're interviewing for infra or platform, expect more on scale and reliability: multi-tenant isolation across health systems, handling Epic's integration quirks, and what your on-call story looks like when a hospital can't get its notes during morning rounds. A note that arrives an hour late is close to useless, so tail latency and graceful degradation are fair game.

## The ML and evaluation round

ML and applied-science candidates get a round that goes deep on how you'd know the system is any good. That is harder than it sounds, because "is this note correct" has no clean loss function. Interviewers want to see that you've thought about evaluating generative output in a domain where mistakes are expensive.

Questions land like this:

- "How would you measure whether a generated note is clinically accurate?"

- "How do you catch a hallucinated medication, or a dosage the clinician never said?"

- "Word error rate in a noisy exam room is high. How would you measure it and bring it down?"

Strong candidates bring up human review, reference notes, structured extraction you can check against the transcript, and the gap between metrics that look nice on a dashboard and metrics a physician trusts. If you've worked on retrieval, speech recognition, or LLM evaluation, this is where it pays off. Vague answers about "using an LLM to grade it" get taken apart fast.

## Behavioral, with real stakes

The values round is not the soft round. Abridge cares a lot about judgment and ownership because the domain punishes carelessness. Expect prompts like "tell me about a time you shipped something where being wrong had real consequences," or a scenario: a clinician reports that the note dropped an important detail, walk me through what you do in the next hour and over the next week.

They're looking for people who take the complaint seriously, who don't get defensive, and who can hold two ideas at once: the ML reality of an imperfect model, and the human reality that this goes in a patient's chart. Humility reads well here. So does having genuinely cared about a user before.

## Compensation and what to expect

Abridge pays competitively for a late-stage, heavily funded AI company, which means base salaries in the band you'd see at other top-tier startups plus equity that could matter a lot given the valuation trajectory. Specific numbers move constantly and vary by level, location, and whether you're on a research, product, or infra track, so treat any single figure with suspicion and pull current ranges from Levels.fyi or straight from the recruiter, who tends to be candid.

The equity conversation is worth having carefully. At a multi-billion-dollar valuation with fast revenue growth, the upside is real, but so is the dilution and the tax math on private shares. Ask about the strike price, the last 409A, and the vesting schedule, and do your own math rather than anchoring on the headline valuation. None of this is tax advice; check the specifics with someone who does this for a living.

## How to prep without wasting your time

Sharpen the fundamentals that actually show up: tries and string algorithms, streaming and sliding-window problems, and the ability to design a real-time pipeline from microphone to signed note. If you're on the ML side, be ready to talk about evaluating generative and speech systems in concrete terms rather than slogans. Read a little about ambient clinical documentation and how Epic integrations work, so the domain isn't foreign when the design question lands.

And have a real reason for wanting to be here. The people who do best at Abridge aren't the ones with the flashiest model resumes. They're the ones who can look at a generated note and immediately picture the clinician who has to trust it, and who treat that trust as the actual engineering problem to solve.