Principal Machine Learning Engineer

Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer.

Contributions to open-source ML or systems libraries.

Background in scientific computing, compilers, or GPU kernels.

Experience with RLHF pipelines (PPO, DPO, ORPO).

Experience training or deploying multimodal or diffusion models.

Experience with large-scale data processing (Apache Arrow, Spark, Ray).

ML systems (training, inference, evaluation) are reliable, scalable, and meet defined performance targets.

Models deployed to production achieve measurable quality improvements and meet user-impact goals.

Production issues are proactively monitored, debugged, and resolved with clear root-cause analysis.

Team and cross-functional collaborators benefit from clear guidance, best practices, and scalable ML solutions.

Research-to-production cycles are efficient, safe, and continuously improve the product experience.

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

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.