Responsibilities
- Build core ML systems that power a proactive, long-horizon AI product.
- Own work end-to-end: data preparation, training, evaluation, inference, and iteration.
- Turn research ideas into working systems that run reliably in production.
- Debug model failures and system issues using real production signals.
- Iterate quickly: ship, measure outcomes, refine, and repeat.
- Collaborate closely with research, product, and engineering to deliver real user impact.
- Mentor and review work from other ML engineers through example and technical judgment.
- Work under real production constraints: latency, cost, reliability, and safety
Requirements
- You have built and shipped ML systems used by real users.
- You understand how modern ML models behave — and misbehave — in production.
- You write strong, production-quality code and think in systems, not scripts.
- You take ownership, work independently, and push work across the finish line.
- You learn fast, communicate clearly, and improve through iteration.
How We Work
The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product
Interview process
If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews. Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite. We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
