6 jobs for "Tech Lead, Research Scientist, Generative AI"

Tech Lead, Research Scientist, Generative AI

At TikTok - San Jose

Posted on May 22

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Tech Lead Research Scientist/Software Engineer, TikTok-Neural Graphics and World Models

At TikTok - San Jose

Posted on Aug 7

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Research Scientist Graduate (Ads Creative) - 2027 Start (PhD)

At TikTok - San Jose

Posted on Oct 27

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Research Scientist Graduate (Ads Creative) - 2027 Start

At TikTok - San Jose

Posted on Aug 3

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Director, Data Scientist - Generative AI Systems

At Capital One - McLean · New York

Posted on Aug 30

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Director, Data Scientist

At Capital One - McLean · New York

Posted on Jun 4

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TikTok

Tech Lead Research Scientist/Software Engineer, TikTok-Neural Graphics and World Models

San Jose$254,400 – $588,000Posted Aug 7, 2026

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Why you should apply for a job to TikTok:

4.5/5 in overall job satisfaction
4.5/5 in supportive management
100% say women are treated fairly and equally to men
100% would recommend this company to other women
100% say the CEO supports gender diversity

Ratings are based on anonymous reviews by Fairygodboss members. See all reviews ›

Responsibilities

About the Team
Join the AI-3D engine team, where we push the boundaries of digital interaction by developing a proprietary next-generation neural engine for games and other interactive content experiences across TikTok and future creative platforms.
Our goal is to move beyond traditional graphics pipelines, where geometry, animation, physics, materials, lighting, and rendering are primarily driven by handcrafted systems. We are exploring an AI-native graphics engine in which learned systems can generate and represent scenes, animate characters, simulate behavior, synthesize visual output, and continuously update interactive worlds in response to user intent and changing context. This engine should enable digital worlds that are persistent, controllable, physically coherent, editable, and responsive.

We are looking for a Tech Lead Research Scientist / Engineer to develop AI models, neural representations, data strategies, and evaluation methods for our neural graphics system.

This is a hands-on research role for candidates who combine current ML expertise with depth in 3D or graphics domain. Depending on your primary focus track, you may define research directions and build AI models, data strategies, evaluation systems, or interactive prototypes across generative modeling, world models, 3D modeling and representations, animation, simulation, and rendering.

Strong candidates combine research judgment with hands-on engineering ability: they can identify an important modeling problem, translate it into concrete research and engineering milestones, run rigorous experiments, and work with engineering teams to turn successful results into interactive, reliable engine capabilities.

Job Responsibilities
- Lead research directions in one or more areas of neural graphics, world models, 3D modeling, animation, simulation, or rendering.
- Develop, train, adapt, and evaluate AI models and representations for interactive graphics and world-generation systems.
- Define data and evaluation strategies that connect model behavior to quality, controllability, coherence, and interactive performance.
- Build research prototypes and collaborate with engineering teams to bring successful models into real-time engine workflows.
- Analyze results, identify technical risks, and translate research findings into clear next steps for model, data, and system development.
- Produce reproducible research artifacts, communicate technical direction, and mentor team members.

Qualifications

Minimum Qualifications
- PhD/MS, or equivalent research experience in machine learning, computer graphics, robotics, applied mathematics, or a related technical field.
- 5 years of relevant research or industry experience in neural graphics, generative AI, computer graphics, world models, or a closely related domain; doctoral research may count toward this experience.
- Strong, up-to-date knowledge of modern machine learning, with deep expertise in at least one area of model architecture, training, data strategy, evaluation, or scaling and hands-on experience training or adapting generative models such as variational autoencoders, latent tokenizers, diffusion or flow-matching models, diffusion transformers, autoregressive models, or multimodal transformers.
- Engineering fluency with ML frameworks such as PyTorch or JAX, with practical understanding of model development, representation learning, model scaling, distributed training, and pre-training and post-training tradeoffs.
- Deep expertise in at least one of the following AI-for-graphics areas: 3D modeling, asset generation, or 3D representations; animation, motion generation, or character behavior; simulation, physical AI, or neural simulation; rendering or neural rendering; world models or video prediction; or graphics and game-engine workflows.
- Demonstrated ability to formulate original hypotheses, design controlled experiments, and connect data composition, model behavior, and evaluation results to technical decisions.
- Ability to collaborate with engineering teams to translate research prototypes into engine pipelines while preserving model quality, controllability, and interactive performance.

Preferred Qualifications
- Publications at leading research venues such as SIGGRAPH, CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, RSS, CoRL, or related top-tier conferences.
- Experience creating novel model architectures, training recipes, data strategies, or evaluation methods for AI-for-graphics systems.
- Experience with 3D or world generation conditioned on text, images, video, actions, or structured scene inputs, including controllability or multimodal alignment.
- Experience with reinforcement learning, preference optimization, imitation learning, or model-based control for interactive world models, where relevant to the focus track.
- Experience moving models into real-time graphics or engine workflows, including distributed training or inference optimization for interactive use.

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:

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.

Responsibilities

Qualifications

Qualifications

Minimum Qualifications
- PhD/MS, or equivalent research experience in machine learning, computer graphics, robotics, applied mathematics, or a related technical field.
- 5 years of relevant research or industry experience in neural graphics, generative AI, computer graphics, world models, or a closely related domain; doctoral research may count toward this experience.
- Strong, up-to-date knowledge of modern machine learning, with deep expertise in at least one area of model architecture, training, data strategy, evaluation, or scaling and hands-on experience training or adapting generative models such as variational autoencoders, latent tokenizers, diffusion or flow-matching models, diffusion transformers, autoregressive models, or multimodal transformers.
- Engineering fluency with ML frameworks such as PyTorch or JAX, with practical understanding of model development, representation learning, model scaling, distributed training, and pre-training and post-training tradeoffs.
- Deep expertise in at least one of the following AI-for-graphics areas: 3D modeling, asset generation, or 3D representations; animation, motion generation, or character behavior; simulation, physical AI, or neural simulation; rendering or neural rendering; world models or video prediction; or graphics and game-engine workflows.
- Demonstrated ability to formulate original hypotheses, design controlled experiments, and connect data composition, model behavior, and evaluation results to technical decisions.
- Ability to collaborate with engineering teams to translate research prototypes into engine pipelines while preserving model quality, controllability, and interactive performance.

Preferred Qualifications
- Publications at leading research venues such as SIGGRAPH, CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, RSS, CoRL, or related top-tier conferences.
- Experience creating novel model architectures, training recipes, data strategies, or evaluation methods for AI-for-graphics systems.
- Experience with 3D or world generation conditioned on text, images, video, actions, or structured scene inputs, including controllability or multimodal alignment.
- Experience with reinforcement learning, preference optimization, imitation learning, or model-based control for interactive world models, where relevant to the focus track.
- Experience moving models into real-time graphics or engine workflows, including distributed training or inference optimization for interactive use.

About TikTok

TikTok is the leading destination for short-form mobile video and our mission is to inspire creativity and bring joy.

Size: 10,000+ employees
Industry: Technology: Consumer Internet

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