Member of Technical Staff - Research Engineer, Omni

Required

• We need someone who:
• Owns a task through to done: Takes a loosely defined problem, decides what good looks like, and finishes it well without being chased.
• Research curiosity: Goes deep on why a method works and asks about directions the team has not taken yet.
• Cares about infrastructure quality: Reviews and hardens code before it lands, including code an AI wrote, rather than treating that as someone else's job.
• Run multimodal training experiments end to end, pre- and post-training, and fold the results into the next Omni release.
• Contribute to open research directions such as multimodal MoE and token efficiency for multimodal encoders.
• Share the multimodal data work: pipelines, curation, and the data decisions that feed each release.
• Take research-grade experiment code and harden it into maintainable infrastructure in the team repo.
• Help consolidate Omni's training infrastructure after the latest release: one codebase, one library, and distributed-training choices made and defended.
• Own inference and deployment for Omni models, and work across the vision and audio teams on the connective tissue between data, infra, training, and evals.
• Experience & Capabilities
• 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).