Applied Scientist - AI-3D Creation Engine - Global Frontier Tech Recruitment Program - 2027 Start (PhD)

Top content: Graphics rendering serves as core infrastructure for multimedia applications. Amid the rapid growth of short-video and interactive entertainment, the traditional production pipeline-marked by long cycles, high costs, and steep technical barriers-has severely limited creative expression for mainstream users. In recent years, breakthroughs in AIGC (such as emerging representations like NeRF and 3DGS) have driven 3D generation from experimental research toward commercial deployment, establishing it as a critical foundation for future spatial intelligence.This topic focuses on cutting-edge 3D generation technologies, aiming to evolve the 3D production pipeline from the traditional "modeling-simulation-rendering" workflow to a new AIGC-driven paradigm: "AI Objects - AI Scenes - AI Animation - AI Rendering." Through this transformation, the project seeks to drastically lower barriers to 3D creation, fully empower the interactive entertainment ecosystem, and build a robust technical foundation for spatial intelligence.

Topic Challenges:
-Insufficient 3D data: High-quality native 3D model data remains severely scarce globally (fewer than 10 million assets), lagging far behind image, text, and video data. This creates a critical data bottleneck for 3D generation. To meet massive generation demands, we must develop more efficient data filtering and collection pipelines, and solve the challenge of extracting high-quality 3D and interactive data from massive short videos and livestreams at low cost.
-Extremely high barriers and complexity in 3D foundation model training: 3D generative models are true foundation models with extremely high training barriers, requiring deep expertise in distributed multi-GPU training and extensive iterative experimentation. In practice, we must resolve complex engineering challenges including long-sequence memory management and compute resource allocation, while achieving optimal algorithmic balance between photorealistic quality, multi-view 3D consistency, and generation controllability.
-Lack of physical interaction and editing capabilities in AI Rendering: Despite rapid advances in AI rendering (e.g., 3DGS), real-world deployment is limited by compute power, visual quality, and consistency. For instance, Gaussian splatting struggles with lighting interaction and physical simulation. Moreover, without explicit geometry like traditional 3D meshes, AI rendering exhibits clear limitations in handling complex surfaces (e.g., hair), supporting fine-grained editing, and maintaining physical consistency.
-Effective integration of advanced video generation into 3D rendering: Video generation models excel in dynamic capture and realism but often lack 3D spatial consistency and physical controllability. A core future challenge is to clarify the interplay between video generation and 3D engine rendering, effectively transfer powerful spatial-temporal semantic priors from video models into 3D rendering, compensate for implicit generation limitations, and transition from passive video generation to active interactive 3D scene creation.

Qualifications

Minimum Qualifications:
-Individuals who are completing or recently completed a PhD in Software Development, Computer Science, Computer Engineering, or a related technical discipline.

Preferred Qualifications:
-Modeling experience in one or more of the following areas: Ads, Search engine, Recommender System, NLP/CV.
-Have a solid foundation in algorithms related to LLMs, including but not limited to comprehensive learning and practical experience in areas such as single-modal/multi-modal LLM application and deployment.
-Strong publications record in top conferences or journals (e.g., ICLR, NeurIPS, ICML, ACL, EMNLP, NAACL, CVPR, ICCV and ECCV)

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $156000 - $387600 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:

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

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

3. Exercising sound judgment.

Client-provided location(s): San Jose, CA

Job ID: TikTok-7628890438180407557

Employment Type: OTHER

Posted: 2026-04-17T20:43:46