Product Engineer

Hamming AI · Austin

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Location: Remote (North America) or Austin, TX

Employment Type: Full-time (no contractors)

Department: Engineering

 

About Hamming AI

Hamming builds three products for voice and chat AI agents: testing/simulation to validate behavior before launch; red-teaming to probe for prompt injection, jailbreaks, PII leakage, and policy violations; and production monitoring/observability to detect failures in live conversations and turn them into regression tests.

 

We are one of the fastest engineering teams in the world. We prod deploy 4x / day. I’m looking for someone who can own reliability and scale across our LLM-enabled platform, shipping precise, outcome-driven improvements to high-availability systems.

— Sumanyu (CEO)

Previously: grew Citizen 4× and scaled an AI sales program to $100Ms/yr at Tesla.

 

Devin Case Study

Ranked #1 Eng team

OpenAI Dev Day 100billion token list

 

What you’ll do

 

You might be a fit if you

  1. Have 3+ years building customer-facing products in a high-velocity environment (startup experience a plus).

  2. Are fluent in TypeScript and comfortable across the stack (React/Next.js + Node services).

  3. Ship quickly but with discipline: you write clear code, strong tests where it matters, and avoid accidental complexity.

  4. Have strong product instincts: you can simplify complex workflows into crisp UX and make good tradeoffs under ambiguity.

  5. Love talking to users, diagnosing friction, and iterating until a feature feels “done.”

  6. Care about reliability: you build with observability, failure modes, and data correctness in mind.

  7. Communicate clearly: written specs, crisp PRs, and decisions that scale across a fast-moving team.

 

Bonus

 

Interesting problems you’ll touch

Our stack

 

Apply

If you want to build the product layer for reliable Voice AI, let’s talk.

 

Send a short note (links to work > resumes) to careers@hamming.ai and tell us about a product you shipped end-to-end: what you built, where it was painful, what tradeoffs you made, and how you knew it worked.

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