Seattle Technology - Algorithm Regular Global Frontier Tech Recruitment Program - 2027 Grad

TikTok; Seattle, Northern (WA, KY); Hybrid; USD 207,000 - 368,000; Full time

Benefits offered by this job

Health insurance

401(k) with company match

Paid parental leave

Disability coverage

Job summary

TikTok in Seattle is seeking an Applied Scientist for Trust and Safety (Multimodal Foundation Model). You will work on state‑of‑the‑art moderation systems, focusing on large‑scale multimodal models, RL‑driven decision making, and robust safety tooling.

The role targets recent PhDs ready to ship impactful research into production. The project emphasizes cross‑modal alignment, agentic moderation, and safety improvements across 200+ languages and content types.

Qualifications

• PhD completed or near completion in CS/AI or related field.
• Strong understanding of LLM research and practical implementation.
• Proficiency in Python, Rust or C++ and DL frameworks.
• Experience with distributed computing and RL/SE for AI.

Responsibilities

• Develop and deploy multimodal safety foundation models for moderation.
• Research agentic moderation systems with RL and tool integration.
• Advance MoE architectures, cross‑modal alignment, and robust evaluation.
• Collaborate with cross‑functional teams to ship scalable safety solutions.

Skills

Python

Rust

C++

Multimodal ML

RL

LLM research

Education

PhD in CS/AI

Tools

PyTorch

DeepSpeed

Megatron-LM

Job description

Discover a career that energizes and excites you every day.

@2026 TikTok

Algorithm

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

Location:

Seattle

Employment Type:

Regular

Job Code:

A103978

Responsibilities

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.About the teamOur Trust and Safety team is fast growing and responsible for building machine learning models and systems to protect our users from the impact of negative content. Our mission is to protect billions of users and publishers across the globe every day. We embrace state-of-the-art machine learning technologies and scale them to moderate the tremendous amount of data generated on the platform. With our team's continuous efforts, TikTok can provide the best user experience and bring joy to everyone in the world.Project Overview, Challenges & ValueWith 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.Key challenges include: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 generation2. 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 stability3. Context engineering and tool collaboration: dynamic context assembly, MCP-based tool ecosystem construction, multi‑source heterogeneous evidence fusion, and GraphRAG strategy retrieval4. Generalization and adversarial robustness: generalization across 200+ languages/strategies, adversarial detection of AIGC content, and design of multi‑dimensional reward signals for few‑shot scenariosProject 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 dependance 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.

Key challenges

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 dependance 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

• 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. 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.

Job Information

The base salary range for this position in the selected city is $207480 - $368220 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

• 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:

About TikTok

TikTok is the leading destination for short‑form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Why Join Us

Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.

We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.

Diversity & Inclusion

TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.

TikTok 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