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.
RL alignment of Pika's video generation models and the reward models that drive them.
Distillation of RL-tuned models is a secondary focus.
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).
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.
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.
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)
Pika empowers creators by building state-of-the-art agentic and multimedia platforms. Our vision is to break down technical barriers to creativity, making real-time generative and intelligent orchestration accessible to all. Join us to shape the next evolution of creative technology!
If you are passionate about advancing RL alignment and generative modeling for video, and want to scale real-time multimodal foundation models, we want to hear from you.
Skills Required
- 2+ years of 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
- Experience with PyTorch
- Experience with multi-node distributed training
- Experience developing reward models for visual generation, including VLM-as-judge or large-scale preference data collection
- Distillation expertise, including distribution matching, consistency, or adversarial approaches, ideally for video models
- Familiarity with video-specific failure modes, including temporal drift, motion, and physics realism
What We Do
An idea-to-video platform that brings your creativity to motion
