Research Scientist, Post-Training — Video Generation

Excerpt from the original posting

About the Role

At Pika, we are pioneering the next generation of creative infrastructure built around real-time, multimodal generation and intelligent agentic platforms. We are seeking Research Scientists with expertise in RL post-training and generative modeling for large-scale video generation. The focus is on refining Pika's video generation models using RL alignment and building robust video reward models. This is a staff and lead-level opportunity.

As a key member of our research team, you will own RL-based post-training for video diffusion/flow-matching models, develop state-of-the-art reward models, and lead post-training evaluation across human and automated metrics. You will collaborate closely with engineering and product teams, shaping the frontier of real-time creative and agentic video platforms.

Scope
- RL alignment of Pika's video generation models and the reward models that drive them.

- Distillation of RL-tuned models is a secondary focus.

Responsibilities
- Run RL post-training (preference optimization, online RL against learned rewards) for video diffusion/flow-matching models at multi-node scale.

- Build video reward models: define target evaluation dimensions, design/configure preference data collection workflows, train and validate learned judges, and safeguard against reward hacking.

- Own post-training evaluation, including human preference studies and their correlation with automated metrics.

- Distill RL-tuned models to efficient few-step samplers while preserving alignment gains (secondary focus).

What We’re Looking For
Required
- 2+ years hands-on research experience in post-training or generative modeling.

- RL or preference-optimization experience on generative models with evidence of model improvement.

- Strong grounding in diffusion or flow-matching models, PyTorch, and multi-node distributed training.

Preferred
- Experience developing reward models for visual generation, including VLM-as-judge or large-scale preference data collection.

- Distillation expertise (distribution matching, consistency, adversarial approaches), ideally for video models.

- Familiarity with video-specific failure modes: temporal drift, motion and physics realism.

What We Offer
- Competitive salary and substantial equity in a high-growth startup

- Full health benefits + 401k matching and more

- Collaborative, mission-driven team environment with significant growth opportunities

- Flexible on-site/remote hybrid (HQ in Palo Alto, CA)…

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