Efficient ML Graduate (AI Platform)- 2026 Start (PhD)

Position Overview

Join the Vision Engineering Team at TikTok, a leader in delivering cutting‐edge GenAI technologies across global products. In this role you will develop algorithm acceleration techniques for text-to-image and text-to-video models, drive innovations in generative models, and collaborate cross-functionally to optimize high-performance vision systems. Successful candidates must be ready to onboard by the end of 2026. (Location: San Jose • Team: Technology • Employment Type: Regular • Job Code: A76271)

Compensation: The base salary range for this role is $136800 - $259200 annually, with potential additional bonuses, incentives, and restricted stock units based on qualifications and experience.

Key Responsibilities

• Develop algorithm acceleration technologies for text-to-image/text-to-video models using techniques such as knowledge distillation, dynamic MoE routing/sparse attention, and low-bit quantization to achieve significant efficiency gains.
• Lead generative model innovation, focusing on diffusion acceleration (sampling step reduction/latent optimization) and autoregressive model efficiency.
• Collaborate cross-functionally to identify performance bottlenecks, apply algorithmic breakthroughs to vision models, and enhance TikTok’s product capabilities.

Required Qualifications

• Final year Ph.D. candidate or recent Ph.D. graduate in Computer Science, Engineering, or a quantitative field.
• Proficient in C++/Python and experienced in high-performance coding.
• Expertise in diffusion models (e.g., Stable Diffusion, DiT) with a deep understanding of computational bottlenecks and model optimization techniques.
• Proven experience in at least one domain such as model compression (quantization/knowledge distillation), efficient architectures (MoE/sparse attention), or generative alignment (RLHF/DPO).
• Excellent communication and teamwork skills with the ability to thrive in a fast-paced work environment.

Preferred Qualifications

• Kaggle competition achievements, publications at ICML/NeurIPS/CVPR, or notable open-source contributions (e.g., HuggingFace Diffusers optimization).
• Research experience in GenAI and MLsys areas.
• Familiarity with open-source deep learning frameworks such as PyTorch, DeepSpeed, or Jax.

Benefits & Perks

• Compensation: Attractive base salary paired with opportunities for bonuses, incentives, and stock units.
• Day one access to medical, dental, and vision insurance.
• 401(k) savings plan with company match.
• Paid parental leave, short-term and long-term disability coverage, life insurance, and wellbeing benefits.
• 10 paid holidays, 10 paid sick days, and 17 days of Paid Personal Time (prorated upon hire with accruals increasing by tenure).