Remote Jobs RockRemote Jobs Rock

Research Scientist, Post-Training — Video Generation

🕒 7 days ago
Reinforcement LearningPost-trainingPreference OptimizationGenerative Modeling
Apply Now

📜 Description

  • Run multi-node RL post-training for video diffusion and flow-matching models.
  • Develop video reward models, from evaluation dimensions and preference-data workflows to training, validation, and safeguards against reward hacking.
  • Lead post-training evaluation using human preference studies and automated metrics.
  • Distill RL-tuned models into efficient few-step samplers while preserving alignment gains.
  • Collaborate with engineering and product teams on video generation platforms.

🛠️ Requirements

  • 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.

✨ Benefits

  • 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)
Full job description

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.

Own the data strategy for foundation-model pre- and post-training, lead research on data quality and evaluation, and guide research engineers building scalable data workflows for autonomous-driving models.

Data-centric AIFoundation ModelsPre-trainingPost-training
Freenome

Staff Computational Biologist

📅 Aug 5
Freenome👥 201 - 500 employees🏢 Biotechnology

Leads computational research for early cancer detection, analyzing molecular and clinical data, developing high-throughput NGS pipelines, and partnering across teams to improve assay performance and diagnostic utility.

Computational BiologyNGS Assay DevelopmentBioinformatics PipelinesHigh-throughput NGS Data Processing
Freenome

Staff Computational Biologist

📅 Jul 23
Freenome👥 201 - 500 employees🏢 Biotechnology

Lead computational analyses and statistical modeling of cancer molecular signatures across genomic and other assay data, partnering with scientists to improve research models and advance diagnostic products toward clinical use.

Computational BiologyCancer BiologyGenomicsStatistical Modeling
DigitalOcean

Staff Forward Deployed Applied Scientist (Agentic AI)

📅 May 8
DigitalOcean👥 1001 - 5000 employees🏢 Computer Software

Design and deploy production-ready agentic AI systems for customer challenges, combining multi-agent architectures, evaluation and safety frameworks, and direct collaboration with customers and AI product teams.

Applied AIMachine LearningData ScienceMulti-agent Orchestration
Datadog

Staff Research Scientist - Foundation & World Models

🔥 12 hours ago
Datadog👥 5001 - 10,000 employees🏢 Computer Software

Drive hands-on research in foundation and multimodal models, training and evaluating large-scale models on distributed-system telemetry and translating research advances into product capabilities.

Foundation ModelsWorld ModelsMultimodal LearningGenerative Modeling

Trusted by Remote Workers