Member of Technical Staff - Research Engineer, Omni

Hands-on experience training ML models in at least one non-text modality (vision is the most likely path; audio is rare and can be learned here).

A foundational understanding of how multimodal training actually works, below the research layer, and the ability to explain it from your own past work.

Comfortable sitting between research and engineering: you run experiments and harden the code behind them.

Able to take training infrastructure and inference from experiment code to something maintained and production-ready, and willing to own that path.

Where we would most like to see unusual strength: enjoying the infrastructure and inference side, having consolidated or rebuilt a training stack before, and arriving with your own opinions on libraries and distributed paradigms.

Audio modality and speech modeling experience.

Prior inference or deployment experience, including on-device inference.

Exposure to multimodal MoE or encoder token-efficiency research.

A publication record or academic research background; what matters is whether you understand what you built.

By the end of your first year, you run Omni model training end to end, including its infrastructure, inference, and half of its data work, and the team's research bandwidth has grown because of it.

You have driven a new capability, such as multimodal MoE, into a shipped release.

The post-release infrastructure consolidation is finished and trusted by both the vision and audio teams.