Founding Engineer

OpenWhispr; San Francisco (CA); On-site; USD 140,000 - 180,000; Full time

Benefits offered by this job

Equity

Unlimited PTO

Job summary

OpenWhispr is hiring engineers to own large parts of the product from idea to production. You’ll ship end-to-end features, speak directly with users, and fix issues before they’re raised. The role blends cloud and on-device work, with a strong bias for practical, high-quality experiences.

You’ll be based in San Francisco or Chicago, in person, with visa sponsorship not offered. The pace is fast, and you’ll join a team building voice-first AI software.

Qualifications

• Shipped real product with users and can explain what you started, what broke, and what you did about it.
• Experience running parallel AI workstreams with high quality.
• About five years of real engineering experience.

Responsibilities

• Take large, fuzzy problems and drive them all the way to production.
• Work directly with users, talk to them, and fix issues before anyone files an issue.
• Own major parts of the product and continue to evolve the system across cloud and local deployments.

Job description

Own big parts of the product, from first idea to production, and help decide what engineering looks like at OpenWhispr.

Hi, I'm Gabe, the founder of OpenWhispr.

I think voice is about to become one of the main ways people work with AI. Not talking to a chatbot, but saying what you actually mean and having software turn it into action. For that to work, people need to trust it with what they say. So we build in the open, and we give people a real choice: run everything locally on your own machine, bring your own API keys, or use our cloud when you want the best accuracy and speed. We're not anti-cloud. We just think the choice should be yours.

The other thing we believe is that a great consumer product has to be opinionated in its design, and that we should still build for the tinkerer. You can edit your prompts, pick your models, and customise your own voice pipeline. Holding both of those at once is most of the challenge, and most of the fun.

I built the first version on my own. It's open source, it's been downloaded about 150,000 times, and the people who use it tell us loudly when something breaks. That's a privilege. Most products never get anyone to care that much. Now I'm hiring the engineers who'll own big parts of it with me.

What you'd work on

You take large, fuzzy problems and drive them all the way to production. No tickets, and no one writing the spec for you. You'll use the product every day, talk to the people who use it, and fix what you find before anyone files an issue.

The engineering problems are genuinely interesting. On our cloud, it's how we get the best accuracy across every language with the fastest inference. On device, it's how we make local speech-to-text and local language models run well on whatever Mac, Windows or Linux machine someone has. In between, it's how we let people bring their own keys without the product getting complicated. And underneath all of it, it's shaving latency out of the voice pipeline while the experience stays simple and beautiful.

Right now that looks like offline meeting recording and speaker identification, the local model pipeline, speech to action inside whatever app you're in, and the calendar and email integrations.

How we work

We have a strong bias for action. If something's broken, fix it. If a user is about to hit a problem, tell them before they find it. If we get a bill wrong, refund it before anyone complains. The same goes for each other: share what you know before someone has to ask, and flag a blocker the moment it appears.

We lead with context. You get the full picture, then the ownership and agency to go and deliver it with the team. Everyone runs several AI-assisted workstreams at once, and the skill that matters most is keeping context and quality across all of them. No AI slop. Everything gets reviewed, tested and refined before it ships, and anything you share with the team is already clear by the time it lands.

Getting to 90% is easy now. We spend our energy on the last 10%, and we do the thinking for the user so they don't have to.

You'll do well here if

• You've shipped something real that people actually used: a company, a product, a side project with users. You can tell us what you started, what broke, and what you did about it.
• You already run parallel AI workstreams and can walk us through exactly how you keep quality high while you do it. Not “I use Cursor.” The actual system.
• You have taste. You notice when something works but doesn't feel right, and you'd rather simplify than add.
• You communicate before people have to ask, and you write things down.
• You've got around five years of real engineering behind you. Less is fine if the evidence is unusual.

Nice to have

None of these are required, but they help.

• You've worked at a VC-backed startup before: a YC company, a Series A or B, a16z Speedrun, that kind of environment. If not, you at least know you want a high-ownership, high-velocity role, and you can say why.
• You've worked on voice in some form: voice AI, speech-to-text, audio pipelines, real-time systems.
• Desktop apps, open-source work people actually use, or AI agents, evals and model infrastructure.

The honest bits

This is in person in San Francisco or Chicago. Not remote. If you're not in one of those cities you'd need to move, and we can't sponsor visas.

The pace is real and our users complain loudly. If that sounds energising rather than exhausting, keep reading.

First 90 days

In your first 30 days you'll ship a real product surface end to end. By 90 you'll own a major part of the product, and people will be asking you how it should work.

Comp and time off

$140K to $180K plus meaningful equity, and unlimited PTO. We'll make sure you actually take it.

How it goes

A call with me. Then a deep dive on something you've built. Then a day working together in person on a real problem. We move quickly, and we'll tell you where you stand.