Senior Software Engineer

Autena Inc.; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Referral bonus

Job summary

Autena Inc. is building at the frontier of applied AI for the operational core of large, real-world industries. We ship in production with design partners and aim to turn a data flywheel that unlocks seven-figure annual value for thousands of enterprises.

You’ll own the product surface and the supporting services, shaping how work is reviewed, corrected, and trusted. This role demands bold product opinions and a pragmatic, honest approach to complex probabilistic systems.

Qualifications

• Built end-to-end product at an early-stage company.
• Strong in Python and able to move quickly in unfamiliar codebases.
• Comfortable shaping product decisions and arguing for them.
• Honest about the limits of the system and willing to surface failure modes.
• Experience shipping probabilistic outputs in real-world apps.

Responsibilities

• Own the product surface and the underlying systems from schema to pixel.
• Define how AI-generated work is supervised, surfaced, and trusted by users.
• Develop the domain model that the product rests on and iterate with customers.

Skills

End-to-end product ownership

Python

FastAPI

Codebase navigation

Product decision making

Tools

Python

FastAPI

Celery

PostgreSQL

Redis

AWS

React

Job description

The opportunity

We’re building at the frontier of what applied AI can do inside real work — the operational core of an industry that moves the physical economy. One of the largest on earth, essential, and almost entirely untouched by modern AI. It still runs on people, spreadsheets, and software written before the internet.

We went vertical first on purpose. Vertical is where the hard problems live: messy inputs, real consequences for being wrong, decades of institutional knowledge nobody wrote down. Anything that works here has been tested against reality in a way horizontal tooling never is.

And it doesn’t stay vertical. The systems we’re building — how work gets decomposed, verified, corrected, and learned from — aren’t specific to one industry. They’re specific to work. The vertical is the proving ground. The reapplication is the company.

We’re deliberately quiet about which industry until we talk. What we’ll say now: it’s enormous, it’s overlooked, and the incumbents aren’t coming. You’ll get the full picture on the first call.

Why this might be interesting

• A cap table most early-stage companies would envy. Raised at the top quartile of seed-stage rounds by size — 36+ months of runway — from the seed investors who were early in Palantir, Databricks, Anduril, GitLab, Retool, Lyft, Square, DoorDash, Superhuman, and Ironclad.

• Real traction, right now. Live in production with design partners, and the data flywheel is already turning. Demand isn’t the bottleneck — execution is. The market is tens of thousands of enterprises, each worth seven figures a year.

• Founded by multi-time exited operators. The founding team has built and sold multiple software companies and spent years up close with dozens more. You’re joining people who know how this is actually done — not learning it alongside them.

The seat

You own the product surface and the systems beneath it — the layer where AI-generated work meets the human who has to stand behind it.

That layer is the whole ballgame, and it’s badly underexplored. Nobody has good answers yet for how a person supervises a system that’s right most of the time: what you surface, what you let through, what you make someone look at, how you show your work well enough to be believed. You’ll be inventing that, not implementing it.

• The product surface. Where work gets reviewed, corrected, and approved. This is where the product either earns trust or doesn’t.

• The services underneath it. State, jobs, queues, retries, and the boring-critical machinery that makes probabilistic output durable rather than a nice demo.

• The domain model everything stands on. Get the core objects wrong early and every team pays for it for years.

Not a feature-factory seat. The team is small enough that you own surfaces end to end — schema to pixel — and close enough to customers that you’ll hear directly when you got it wrong.

Who you are

The bar is judgment and range. You don’t need to know our industry — we’ll teach you the domain.

• You’ve built product end to end at an early-stage company. (Hard requirement.) Schema through interface, in real users’ hands, where you owned what broke. Big-company depth in one narrow layer isn’t the profile.

• Strong in Python, and fast in an unfamiliar codebase. You don’t need every line of our stack on your résumé — you do need to be dangerous in it inside a month.

• You’re comfortable building around systems you can’t fully predict. Probabilistic output fails in ways CRUD apps don’t. If that reads as an interesting design constraint rather than someone else’s problem, keep going.

• You have product opinions and you argue for them. You want to watch people work and push back on a spec that doesn’t survive contact with reality.

• Intellectually honest about the limits of your own systems. You volunteer failure modes unprompted. We weight this as heavily as raw capability.

The stack

Python · FastAPI · Celery · PostgreSQL · Redis · AWS · React

Referral bounty

Know someone exceptional? Introduce us. If we hire them into a full-time role, we’ll send you $5,000.