Applied Researcher, Vision Language Models/VLM - TikTok

TikTok Inc.; San Jose (CA); On-site; USD 254,000 - 588,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) with company match

Paid parental leave

Short-term disability

Long-term disability

Life insurance

Wellbeing benefits

Paid holidays

Paid sick leave

Paid Personal Time

Job summary

TikTok Inc. Foundations and Intelligence Service R&D is expanding and seeks researchers in LLM, VLM and Omni Model domains with experience in pretraining, evaluation, data processing and RL-based alignment to push state-of-the-art foundations for real-world TikTok applications.

You will lead research, collaborate with cross-functional teams, optimize multimodal models with OCR/captioning features, and explore efficient architectures to scale downstream TikTok services.

Qualifications

• PhD in CS/DS/AI or related field.
• Experience with single-/multi-modality LLM/VLM pretraining and applications.
• Proficiency in Python, Rust or C++ and DL frameworks (PyTorch, DeepSpeed, Megatron, vLLM).

Responsibilities

• Lead, collaborate and execute on research that pushes forward state-of-the-art in multimodal reasoning and generation research
• Enhance VLM with features like OCR and captioning for TikTok business applications
• Explore model architecture and inference-efficient design to enable scaled downstream TikTok applications
• Work closely with cross-functional teams to plan and implement projects harnessing VLMs for diverse purposes and domains
• Extend insights from industry to academia

Skills

Python

Rust

C++

PyTorch

DeepSpeed

Megatron

vLLM

Education

Ph.D in Computer Science, Data Science, AI or related field

Job description

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/VLM 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, collaborate and execute on research that pushes forward state-of-the-art in multimodal reasoning and generation research


• Enhance VLM with specialized features like OCR and captioning to optimize performance for TikTok business applications


• Explore the model architecture and inference-efficient model design to enable scaled application in downstream TikTok business


• Work closely with cross-functional teams to plan and implement projects harnessing VLMs 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


• 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.).

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 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 $254400 - $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.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

• Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;


• Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and


• Exercising sound judgment.