ML Engineer – AI Safety & Multimodal Moderation

TikTok; San Jose (CA); On-site; USD 162,000 - 388,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k) with company match

Parental leave

Disability coverage

Wellbeing benefits

10 paid holidays per year

10 paid sick days per year

17 days of Paid Personal Time

Job summary

TikTok is seeking a Machine Learning Engineer, Business Governance, to build trust and safety systems in a fast-paced environment in San Jose. You will work on multimodal content understanding, risk detection, and scalable moderation using cutting-edge AI tech and large models.

You will own training and deployment of LLMs/MLLMs, applying post-training techniques like SFT and RLHF, while collaborating with cross‑functional teams to deliver robust safety solutions.

Qualifications

• Bachelor degree or above in CS, software engineering, AI, math, or related fields.
• Strong understanding of ML/DL, transformers, LLMs and MLLMs.
• Hands-on Python and PyTorch with distributed training experience.
• Proven ability to solve complex problems in cross-functional teams.

Responsibilities

• Develop content safety algorithms for TikTok, Lemon8 and short-form platforms across multimodal data.
• Leverage ML/DL and large-model tech to improve moderation pipelines and robustness.
• Own training and application of LLMs/MLLMs for content safety use cases, incl. SFT, RLHF, DPO, safety alignment.

Skills

Python

PyTorch

Distributed training

ML/DL concepts

Transformer architectures

LLMs / MLLMs

Cross-functional collaboration

Education

Bachelor's degree or above in CS, SWE, AI, Math, or related fields

Tools

Data processing

Job description

TikTok is seeking a Machine Learning Engineer, Business Governance, to build trust and safety systems in a fast-paced environment in San Jose. You will work on multimodal content understanding, risk detection, and scalable moderation using cutting-edge AI tech and large models.

You will own training and deployment of LLMs/MLLMs, applying post-training techniques like SFT and RLHF, while collaborating with cross‑functional teams to deliver robust safety solutions.