Responsibilities
Our Foundations and Intelligence Service R&D team is fast growing and responsible for building state-of-the-art foundation models, such as LLM, VLM and Omni Models. Our mission is to build a bridge for collaboration between foundation models and downstream business scenarios, and use foundation model powered world knowledge to enhance better user experiences across TikTok, including content moderation, search and recommendations, client AI, etc. We are looking for researchers in LLM, VLM and Omni Model domain who are experienced in single/multi-modality LLM pretraining and applications, including evaluations, data processing and recipes for pre-training and post-training, reinforcement learning based alignment, efficient training and inference. There are no doubt a lot of unsolved problems in the LLM domain which could have a huge impact on industry and academia. In TikTok, we have real applications, resources and patience for technology incubation. Your main responsibilities will be - Lead the incubation of next-generation, high-capacity LLM solutions for TikTok business, identify and define both short and medium term objectives; - Design methods, tools, data recipes and experiments to push forward state-of-art in large language models; - Explore new model architecture and inference-efficient model design for LLM applications to scale impact on business - Work closely with cross-functional teams to plan and implement projects harnessing LLMs for diverse purposes and vertical domains - Extend the insights and impact from industry to academia
Qualifications
Minimum Qualifications - Ph.D in Computer Science, Data Science, Artificial Intelligence, or a related field - 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.). 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. - 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 - 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.
Job Information
[For Pay Transparency]Compensation Description (Annually)
The base salary range for this position in the selected city is $308000 - $588000 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).
The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
