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Open nowPosted 60 days ago

Founding Engineer, ML + Full-Stack

autostep2 open roles

Where
San Francisco, CA
Work mode
On site
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Your applicationOpen nowFounding Engineer, ML + Full-Stackautostep · San Francisco, CA

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 60 days ago

The posting

Mission

Raise humanity’s starting point with every act of work.

Humanity should not keep paying the cost of knowledge it has already created.

Autostep measures outcomes from human and AI work, identifies what companies should change, and helps make that change happen. We have real customer pull. We’re hiring two founding engineers to help turn that pull into exceptional, repeatable customer results.

You’ll work across machine learning, full-stack product, forward deployment, and desktop systems. This will be hard. You’ll work directly with mid-market and enterprise customers, ship constantly, and deal with requirements that are messy, incomplete, and sometimes wrong.

Your job is to put structure around that ambiguity, figure out what should actually be built, and ship it.

What you’ll do

  • Build and improve our data + ML pipeline across accuracy, context, speed, evals, algorithms, agents, and cost.
  • Ship new projects, product surfaces, UI, experiments, and features constantly. Small projects and improvements should be able to ship daily.
  • Improve our existing Mac/Windows desktop app across reliability, performance, deployment, and new capabilities.
  • Work directly with customers to clarify messy requirements and environments, question assumptions, define the actual problem, and create as much value as possible.
  • Turn lessons from individual customers into repeatable product improvements that make the next deployment better.
  • Keep engineering clean as you move quickly: requirements, PRDs, Linear, documentation, tests, decisions, and deadlines should stay current.
  • Use AI coding aggressively. We do not care whether you use one agent or twenty. We care whether you understand what shipped and whether it works.

What we’re looking for

You have driven real outcomes through things you’ve built or shipped and can show us what changed because of your work.

Strong Python matters. Our current world includes Python, Jupyter notebooks, TypeScript, React/Next.js, AWS, Supabase/Postgres, Electron, Rust, Vercel, GitHub, and modern AI coding tools.

We care about applied ML, algorithms, evals, agents, LLMs, data systems, systems design, desktop software, UI/product taste, and enterprise infrastructure. You do not need every skill today. You need to learn unusually fast.

You can talk to customers, explain technical systems clearly, test assumptions, and question bad ones. When something goes wrong, you investigate, communicate clearly, and find a path forward.

Degrees are optional. Shipped work matters more.

What you get

  • $100K in LLM/model/compute credits to help improve the product. Create great customer outcomes with it and we’ll expand the budget.
  • Additional model, developer, and startup benefits we’ll show you.
  • Occasional access to invite-only founder/startup events.
  • Surfing when time and conditions permit.
  • A chance to learn deeply across applied ML, AI systems, product, enterprise infrastructure, and some genuinely unusual technical problems.
  • Work directly with the founder at a company backed by YC and Neo.
  • Work alongside investors who advise us, including Walden Yan (Co-Founder, Cognition, $26B), Erik Goldman (Co-Founder, Vanta, $4B), Charles Mourani (Co-Founder, Cherry, $2B), Kabir Barday (Co-Founder, OneTrust, $4.5B), and Kunal Shah (CEO of WhatsApp; Co-Founder, CRED, $4.5B), alongside other reputable enterprise founders and co-founders.

Success looks like

Customers get better outcomes because of what you shipped.

They use it. They get exceptional results. They renew and expand.

Deployments and product quality improve. What you learn from one deployment makes the next deployment faster and better.

This is a high-ownership role from day one. We expect high ownership, speed, and measurable outcomes.

Skills

Python · Machine Learning · Large Language Models · AI Agents · Evals · AWS · TypeScript · React · Electron · Rust · Jupyter · Software Architecture · Software Security · Windows · macOS

About the interview

Phone Screen

References

In-Person Technical Interview(s) in San Francisco Preferable / Remote Interview

  • AI-Native Technical Build
  • Customer and Forward-Deployment Simulation

Final Conversation

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