Applied Scientist - Trust and Safety (Multimodal Foundation Model) - Global Frontier Tech Recruitment Program - 2027 Start (PhD)
San Jose, CASalary not listedAdded to Openbound 25 days ago
Applied Scientist - Trust and Safety (Multimodal Foundation Model) - Global Frontier Tech Recruitment Program - 2027 Start (PhD)
TikTok
Job description
We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.
About the team
Our Trust and Safety team is fast growing and responsible for building machine learning models and systems to protect our users from the impact of negative content. Our mission is to protect billions of users and publishers across the globe every day. We embrace state-of-the-art machine learning technologies and scale them to moderate the tremendous amount of data generated on the platform. With our team's continuous efforts, TikTok can provide the best user experience and bring joy to everyone in the world.
Project Overview, Challenges & Value
With the rapid development of AIGC and the globalization of content ecosystems, content moderation faces three major challenges: evolving policies, surging complexity in multilingual and multimodal content, and upgraded generative adversarial attacks. The traditional "perception → classification" paradigm has reached its limit. This topic focuses on two frontier directions: (1) Multimodal moderation foundation model: We study large-scale MoE architecture training and routing optimization, cross-modal alignment and reasoning for multimodality (text/image/video/audio), Unified Understanding & Generation, and high-quality synthetic data generation for moderation scenarios (self-play / adversarial augmentation). (2) Agentic moderation system: Drawing on advanced agent learning paradigms, it uses reinforcement learning to enhance the agent’s multi-step decision-making capabilities. It dynamically builds moderation context and integrates a flexible tool ecosystem, enabling autonomous planning, tool collaboration, and interpretable closed-loop reasoning. This drives a paradigm shift from passive classification to proactive intelligent decision-making in moderation.
Key challenges include:
1. MoE-based multimodal safety foundation model: training stability and routing optimization for large-scale sparse MoE, cross-modal token alignment, and unified architecture design for understanding and generation
2. RL-driven agentic decision-making: end-to-end training of agent multi-step reasoning and tool-call strategies based on GRPO/PPO, overcoming bottlenecks in sample efficiency and training stability
4. Generalization and adversarial robustness: generalization across 200+ languages/strategies, adversarial detection of AIGC content, and design of multi-dimensional reward signals for few-shot scenarios
Project Value:
1. Technological leadership: The integration of RL, Agentic, and multimodal foundation models represents the frontier of AI today. This topic pioneers their application to large-scale content moderation scenarios, with unique advantages in data volume and real-world feedback loops that are impossible to reproduce in pure academic settings.
2. Business value: Serving content safety for billions of users globally and driving the evolution of moderation from dependance on humans and external APIs towards fully automated agentic moderation. This can directly reduce costs by hundreds of millions of US dollars while improving moderation consistency and response speed.
3. Industry leadership: Mature RL-driven agentic moderation systems do not yet exist in the industry. This topic could hopefully define the technological paradigm for this direction and produce research outcomes with significant industry influence.
Qualifications
Minimum Qualifications - Individuals who are completing or have recently completed a PhD degree in Computer Science, Data Science, Artificial Intelligence, or a related field - Strong understanding of cutting-edge LLM research (e.g., long context, multi modality, alignment research, agent ecosystem, etc.) and possess practical expertise in effectively implementing these advanced systems as a plus - Proficiency in programming languages such as Python, Rust, or C++ and a track record of working with deep learning frameworks (e.g., pytorch, deepspeed, megatron, vllm, etc.). - Strong understanding of distributed computing framework & performance tuning and verification for training/finetuning/inference; Being familiar with PEFT, RL, MoE, CoT or Langchain is a plus.
Preferred Qualifications
Excellent problem-solving skills and a creative mindset to address complex AI challenges. Demonstrated ability to drive research projects from idea to implementation, producing tangible outcomes.
Published research papers or contributions to the LLM community would be a significant plus.
Experience with inference tuning and Inference acceleration. Have a deep understanding of GPU and/or other AI accelerators, experience with large scale AI networks, pytorch 2.0 and similar technologies.
Experience with evaluation of AI systems, LLM application & agent development is desirable.
