Job description

We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company. Successful candidates must be able to commit to an onboarding date by end of year 2027. Please state your availability and graduation date clearly in your resume. About the Team The Intelligent Creation - Global GenAI team focuses on applied research in Generative AI, and delivers intelligent solutions to TikTok, enabling users to make and share creative content in a much easier way. The team has research groups dedicated to multimodal foundation models for content creation, image/video generation and editing, efficient modeling, and world models. Topic Content: As AGI large model technology advances, the way AI creates multimodal images, text, and videos is changing deeply. New creative solutions based on generative AI and agent technologies keep appearing. Multimodal creation large models use cutting-edge methods like full-modal content understanding, AIGC image and video generation, and agentic foundation models to build flexible, efficient, and industry-leading ways to create multimedia content. Through continual training and post-training, these models steadily raise their abilities in content understanding and image/video generation, optimizing the foundational models end to end for creation agent scenarios. Challenges: - Deeply involving in post-training (SFT/RL) of Seed multimodal models and LLM. - Participating in unified modeling for image and video generation, driving model performance improvements, and gaining hands-on experience in model iteration and large-scale training. - Applying agent technology and architecture, optimizing tool-calling and long-horizon task capabilities of agentic foundation models, and conducting in-depth research on agentic RL. Research value: This topic focuses on the multimodal creation transformation in the AGI era, relying on full-modal understanding, AIGC generation, and agentic foundation models to build an efficient and intelligent multimedia creation system. Through ongoing training and model optimization, it constantly pushes forward content generation and understanding abilities, moving AI creation from reactive generation toward autonomous intelligence. This topic combines cutting-edge technology with practical industry value, providing core support for the next generation of intelligent creation. Responsibilities - Develop large-scale, diverse, and interactive multi-modal data generation pipeline. - Develop training pipeline for long-context interactive video generation models. - Advance video generation models to capture long-horizon temporal consistency, realistic physical dynamics, object interactions, and causal relationships from large-scale multi-modal data. - Explore new products with artificial intelligence technology at its core. Minimum Qualifications: - Individuals who are completing or recently completed a PhD in Software Development, Computer Science, Computer Engineering, or a related technical discipline. - Excellent collaboration and interpersonal skills - Highly competent in algorithms and programming; Strong coding skills in Python/C++. Preferred Qualifications: - Experiences in broad GenAI, multimodal foundation models, or Embodied AI areas. - Demonstrated ability to communicate complex technical concepts and collaborate effectively within cross-functional research teams - Proven track record of first-author publications in prestigious venues including CVPR, ICLR, NeurIPS, SIGGRAPH, and ICML

Requirements

Must-haves

Successful candidates must be able to commit to an onboarding date by end of year 2027. Please state your availability and graduation date clearly in your resume.
About the Team
Deeply involving in post-training (SFT/RL) of Seed multimodal models and LLM.
Participating in unified modeling for image and video generation, driving model performance improvements, and gaining hands-on experience in model iteration and large-scale training.
Applying agent technology and architecture, optimizing tool-calling and long-horizon task capabilities of agentic foundation models, and conducting in-depth research on agentic RL.
Develop large-scale, diverse, and interactive multi-modal data generation pipeline.
Develop training pipeline for long-context interactive video generation models.
Advance video generation models to capture long-horizon temporal consistency, realistic physical dynamics, object interactions, and causal relationships from large-scale multi-modal data.
Explore new products with artificial intelligence technology at its core.
Individuals who are completing or recently completed a PhD in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
Excellent collaboration and interpersonal skills
Highly competent in algorithms and programming; Strong coding skills in Python/C++.

Nice-to-haves

Experiences in broad GenAI, multimodal foundation models, or Embodied AI areas.
Demonstrated ability to communicate complex technical concepts and collaborate effectively within cross-functional research teams
Proven track record of first-author publications in prestigious venues including CVPR, ICLR, NeurIPS, SIGGRAPH, and ICML

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmpython

Platform: tiktok
Effort: medium