About the job
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.
Responsibilities
1. 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. Use reinforcement learning to enhance the agent’s multi-step decision-making capabilities, dynamically build moderation context, and integrate a flexible tool ecosystem, enabling autonomous planning, tool collaboration, and interpretable closed-loop reasoning.
3. Overcome bottlenecks in sample efficiency and training stability for end-to-end training of agent multi-step reasoning and tool-call strategies based on GRPO/PPO.
4. Focus on generalization across 200+ languages/strategies, adversarial detection of AIGC content, and design of multi-dimensional reward signals for few-shot scenarios.
Qualifications
Minimum
- 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
- 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.