Applied Scientist - Trust and Safety (Multimodal Foundation Model) - Global Frontier Tech Recruitment Program - 2027 Start (PhD)

About the role

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.

Project Overview, Challenges & Value

The project focuses on two frontier directions: (1) Multimodal moderation foundation model and (2) Agentic moderation system. The key challenges include MoE-based multimodal safety foundation model training stability and routing optimization, cross-modal token alignment, and unified architecture design for understanding and generation. RL-driven agentic decision-making involves 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. Context engineering and tool collaboration involve dynamic context assembly, MCP-based tool ecosystem construction, multi-source heterogeneous evidence fusion, and GraphRAG strategy retrieval. Generalization and adversarial robustness focus on generalization across 200+ languages/strategies, adversarial detection of AIGC content, and design of multi-dimensional reward signals for few-shot scenarios. The project aims to provide technological leadership, business value, and industry leadership in large-scale content moderation.

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.
• 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.

Job Information

[For Pay Transparency] Compensation Description (Annually): The base salary range for this position in the selected city is $212,800 - $450,000 annually. Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units. Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).