Research Scientist, Post-Training — Video Generation
Required skills
- MLLM
- LLM
- VLM
- Audio LM
- real-time generation
- agentic platforms
- multimodal synthesis
- autoregressive models
- diffusion models
- model distillation
Nice to have
- creative infrastructure
- intelligent agentic systems
- scalable
- interactive multimedia experiences
- high-fidelity
- cross-modal
Role summary
Pika is seeking Multimodal LLM Researchers (LLM, VLM, Audio LM) to advance real-time, multimodal generation and agentic platforms. The role involves designing and building foundational technologies, developing novel algorithms for high-fidelity synthesis, focusing on real-time inference, and training/finetuning generative models. Responsibilities include dataset curation, collaboration with engineering and product teams, and publishing research. The ideal candidate has 5+ years of experience, a strong publication record, expertise in LLMs/VLMs/Audio LMs, generative models (autoregressive, diffusion), real-time systems, and programming skills in Python, PyTorch, or TensorFlow.
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)
About Pika
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.
