San Jose Technology - Algorithm Global Frontier Tech Recruitment Program - Intern

TikTok; San Jose (CA); On-site; USD 68,191 - 97,120; Full time

Benefits offered by this job

Health insurance

Life insurance

Paid holidays (10 per year)

Paid sick leave

Housing allowance (if eligible)

Job summary

TikTok is seeking an Applied Scientist Intern for the Global Frontier Tech Recruitment Program in San Jose. This role offers an opportunity to work on advanced AI challenges in multimodal moderation systems, contributing to major technological innovations in the field.

Interns will engage in developing cutting-edge frameworks for content moderation, enabling proactive decision-making and enhancing user safety across platforms. Candidates should be pursuing a PhD and have a strong background in programming and AI technologies.

Qualifications

• Strong understanding of distributed computing and performance tuning.
• Excellent problem-solving skills and creativity in AI challenges.
• Published papers in LLM community are a plus.

Responsibilities

• Tackle complex AI challenges and drive projects from idea to implementation.
• Build multimodal moderation systems using advanced AI paradigms.
• Collaborate in a team environment to enhance moderation decision-making.

Skills

Programming in Python

Strong understanding of LLM research

Proficiency in Rust or C++

Experience with deep learning frameworks

Education

Currently pursuing a PhD in Computer Science or related field

Tools

PyTorch

DeepSpeed

Megatron

Job description

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

Location: San Jose

Employment Type: Intern

Job Code: A219452

Responsibilities

We are looking for talented individuals to join our team in 2027. As an intern, 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. Successful candidates must be able to commit to an onboarding during the summer 2027. Please state your availability clearly in your resume.

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.

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.
3. Context engineering and tool collaboration: dynamic context assembly, MCP-based tool ecosystem construction, multi-source heterogeneous evidence fusion, and GraphRAG strategy retrieval.
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 dependence 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

• Currently pursuing a PhD 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; familiarity 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. 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

The hourly rate range for this position in the selected city is $60-$60.

Benefits

Interns have day one access to health insurance, life insurance, wellbeing benefits and more. Interns also receive 10 paid holidays per year and paid sick time (56 hours if hired in first half of year, 40 if hired in second half of year). Interns who are not working 100% remote may also be eligible for housing allowance. The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

Equal Employment Opportunity

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;
• Exercising sound judgment.

Accommodation

TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://tinyurl.com/RA-request.