Member of Technical Staff - Post Training

Job summary

Responsibilities

Post-training is where a foundation model becomes a product. In this role, you'll own the post-training pipeline for our multimodal models end to end — from data strategy and reward modeling to preference optimization, distillation, and safety tuning — across image, editing, and video.

• Own the full post-training pipeline end to end — from data curation and reward modeling through fine-tuning, preference optimization, distillation, safety tuning, evaluation, and…
• Advance techniques across the post-training stack: SFT, RLHF, RLAIF, DPO, preference learning, and reward modeling to align models with human intent and aesthetic judgment
• Work across modalities: text-to-image, image editing, multi-reference, and video post-training
• Build personalization and customization capabilities that let users adapt our models to their own creative style
• Design and maintain high-throughput fine-tuning and evaluation infrastructure to support rapid iteration across the research team

What they're looking for

• You've owned post-training for a frontier generative model through release (SFT, preference optimization (DPO or RLHF), distillation, safety tuning) with measurable quality wins…
• Deep experience across the post-training stack, not just one slice: reward modeling, preference learning, RLHF/RLAIF, and personalization
• Comfortable working across modalities: text-to-image, image editing, multi-reference, and ideally video
• Strong PyTorch fluency; you write research code that others can build on
• Experience with distillation (LADD, DMD, consistency models, or similar) or with building high-throughput eval pipelines is a strong plus

Tech stack

• PyTorch