Research Scientist, Video Generation Distillation

About Hedra

Hedra is a pioneering generative media company backed by top investors at Index, A16Z, and Abstract Ventures. We're building Hedra Studio, a multimodal creation platform capable of control, emotion, and creative intelligence.

At the core of Hedra Studio is our Character-3 foundation model, the first omnimodal model in production. Character-3 jointly reasons across image, text, and audio for more intelligent video generation — it’s the next evolution of AI-driven content creation.

At Hedra, we’re a team of hard-working, passionate individuals seeking to fundamentally change content creation and build a generational company together. We value startup energy, initiative, and the ability to turn bold ideas into real products. Our team is fully in-person in SF/NY with a shared love for whiteboard problem-solving.

Overview

We are seeking a Research Scientist to lead innovation in video generation distillation, including step distillation, model size reduction, and efficient inference methods. This role focuses on making state-of-the-art video diffusion models faster, lighter, and more deployable without sacrificing quality. The ideal candidate will be experienced in model compression techniques and capable of bridging cutting-edge research with production needs.

Responsibilities

• Research and develop distillation techniques for video diffusion models, including step distillation, layer pruning, and knowledge transfer.
• Optimize models for latency, memory footprint, and energy efficiency while maintaining high generation quality.
• Collaborate with engineering to implement and benchmark accelerated inference pipelines.
• Monitor and evaluate advancements in model compression, quantization, and efficient generative modeling.
• Present findings to the team and contribute to publications or patents where applicable.

Qualifications

• PhD or strong industry experience in Machine Learning, with a focus on model compression, distillation, or efficient deep learning.
• Strong understanding of diffusion models and their training/inference workflows.
• Proficiency in Python and PyTorch; familiarity with performance profiling and optimization.
• Experience with quantization, pruning, and low-rank adaptation techniques is a plus.
• A record of impactful work in model efficiency, either in research or production.

Benefits

• Competitive compensation + equity
• 401k (no match)
• Healthcare (Silver PPO Medical, Vision, Dental)
• Lunch and snacks at the office

We encourage you to apply even if you don't meet every requirement — we value curiosity, creativity, and the drive to solve hard problem