WithCoverage raises $50M, led by Sequoia & Khosla

Engineering at WithCoverage

Artificial Intelligence to Protect the Real World

We build systems that handle tedious, repetitive work—so our team can do what AI can’t. You’ll work closely with the people who use what you build daily, and ship software that affects thousands of real businesses with real problems.

Role
member of technical staff
Stack
aws, vercel, eks, argocd, postgres, iceberg
AI
custom-built harnesses, autonomous coding, self-improving loops
Where
nyc, in office, five days

Changelog

Recent production changes.

live
PRs merged / wk
450
median time to merge
1h
AI calls / wk
402k
AI tokens / wk
5.3B

Today

Admin market domain moved end to end onto REST with retired GraphQL fields

platform

Feature-flag evaluation wired into background workers with rotation-aware reloads

infra

Database credential paths sealed with KMS-backed SSM policy boundaries

infra

Yesterday

First-load JavaScript budgets enforced across every application route

infra

Received import rows coalesced into file-list reads to collapse request chains

platform

Large claim uploads moved to resumable post-submit transfer with safe retries

product

Route-level JavaScript budgets enforced in CI for every first load

platform

Received-file lists batch pending rows into a single API read

platform

ODE LocalStack isolated on tainted on-demand nodes

infra

Mon · Oct 5

Admin cron fleet moved to singleton scheduled functions

infra

Browser uploads stream in resumable parts with progress

platform

Lightweight ODE snapshots stored in encrypted versioned buckets

infra

Live hosted UI editing streams local changes through credentialless dev pods

platform

Lightweight environment snapshots backed by immutable, scoped object storage

infra

Sun · Oct 4

Email connection store collapsed to indexed rows behind advisory-locked migration

infra

Connector profile reads parallelized, dropping tab loads below one second

infra

Certificate templates carry expiration semantics across service boundaries

platform

Sat · Oct 3

Client ownership reads moved onto account indexes, cutting query buffers 90%

infra

Account ownership reads moved onto live indexes, cutting buffer scans 10x

infra

Scoped admin read APIs added exact-version artifact downloads

platform

Durable deletion workflows gained previews, confirmations, and account guards

platform

Account ownership reads stay index-bound under refresh load

infra

Scoped read APIs expose policy and communications data from stored mirrors

platform

Durable data-deletion jobs enforce previewed, guarded erasure

platform

Fri · Oct 2

Browser-direct multipart uploads added across service, gateway, and storage layers

infra

Cross-account workflow change feed records commits by transaction id

platform

Browser-direct multipart uploads resume large files through S3 parts

infra

REST surface consolidated on generated clients and Nest modules

platform

Workflow completion feed pages cross-account changes by transaction id

infra

Browser REST traffic consolidated on a generated typed client

platform

Why this job exists

Engineering is suddenlyvery different.

Companies used to hire people to fuel growth and pay SaaS vendors for software they could use. That's all changed — now companies want to buy outcomes.

Our team builds the platform and tools to enable our risk management experts to scale themselves 2x, 10x, or 100x more than was possible in the past.

We are working to break the scaling laws of services businesses with AI agents. We are a deeply collaborative, in office, hard working and curious team in NYC.

Diagram: AI agents pull insurance documents from a task queue, work them concurrently, and ship finished artifacts without supervision. Interactive: choose a file in the queue to dispatch it to an idle agent, select an agent to see its live log, or select a shipped artifact to preview it.

Diagram: an engineer and a risk expert deploy as a pair into client verticals — defense, hospitality, logistics, energy — build on site, and ship measurable outcomes. Interactive: choose a site to deploy the pair there, or select a deployed site for details.

Diagram: an AI harness loops through run, eval, learn, and update stations. Each lap appends an improvement to the harness changelog, evals flip from fail to pass, and the pass rate climbs from 62 to 94 percent. Interactive: select the next station to drive an iteration by hand, or any station for details.

Diagram: a fleet of AI agents serves a growing wall of client accounts, routing only rare judgment calls to a single risk expert. Accounts per expert climbs from 10 to 100. Interactive: choose an unlit account tile to onboard it, or select the expert or an agent for details.

Real, interesting problems — You'll build AI infrastructure that can read and understand complex documents, ask questions, make decisions, and take actions that have real consequences for our clients' businesses. You'll learn what it takes to build and deploy autonomous agents that manage risk at scale in the real world.

Inside the team

The actual work.

Cole S.Mitch R.Nick O.Taylor A.

engineering

Cole S.
What are you building?
Both the brokerage and the system that runs it — an autonomous risk-analysis platform that takes the manual work off our experts so they stay on the judgment.
Where does your time go?
Mostly leverage: unblocking engineers, writing specs, making the hard technical calls — and building with AI agents myself.
What was hard to get right?
An architecture AI agents can actually build in. Keeping the system clean and consistent enough that an agent, not just a person, can ship real work is the hard part.
Why did you join?
A massive legacy industry barely touched by modern software — and the chance to go deep on a new domain and build it from the ground up.

Leadership

Who you'll build with.

  • JD Ross

    JD Ross

    Co-founder

    An entrepreneur and product leader with a track record of building category-defining companies. He co-founded Opendoor, taking it from inception through IPO, and brings a product-first instinct to everything we build.

  • Naveen Kasthuri

    Naveen Kasthuri

    CTO

    A pioneer in AI-native engineering with a background that blends finance and technology. Previously a senior principal engineer and chief architect in charge of transforming a 700-person R&D org to be AI-native.

Backed by Sequoia & Khosla · Series B

How we hire

A process builtto respect your time.

  1. Technical Deep Dive

    precision and depth

    We go deep on the decisions, the tradeoffs, and what you'd do differently. No trivia, no whiteboard puzzles.

  2. Coding Round with AI

    how you steer the model

    Bring your own AI tooling and pair on a real problem. We don't ban the model or treat it as cheating — using it well is the job. We watch how you prompt, review, debug, and steer it, because that's exactly how you'll ship here.

  3. Onsite — 2–3 Interviews

    systems and product judgment

    A few focused sessions on systems design and product thinking. Architect something real, then reason about what to build, for whom, and why.

Do what AI can't. Build what it can.

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