Multi-Modal ML Engineer for Trust & Safety

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

Benefits offered by this job

Medical insurance

401(k) plan with company match

Paid parental leave

Paid holidays and personal time

Job summary

TikTok is recruiting a multi-modal machine learning engineer for its Trust & Safety team in San Jose. You will build and deploy models to protect users, collaborating with product and data science teams to define objectives and drive safety strategies at scale.

Ideal candidates have experience in ML (CV/NLP/Multimodal), strong Python skills, and a passion for solving large-scale safety problems, with a focus on online model workflows and deployment.

Qualifications

• BS/MS in CS, statistics, OR, applied mathematics or similar with 2+ years of related experience.
• Hands-on experience in ML areas: CV/NLP/Multimodal/Graph/LLM.
• Strong Python coding skills.
• Curiosity towards new technologies and entrepreneurship, good communication and teamwork skills.

Responsibilities

• Lead and build content understanding models for content moderation.
• Work on unimodal/multimodal content classifiers, graph models, representation learning models, and LLM.
• Collaborate with product teams to define business objectives and trust strategy.
• Collaborate with engineering teams to deploy ML models and design scalable online workflows.
• Collaborate with data scientists to address key challenges with data-driven strategies.

Skills

ML areas experience

Python

Communication & teamwork

entrepreneurship curiosity

Education

BS/MS in CS/Statistics/OR/Applied Math or similar

Tools

LLM

Job description

TikTok is recruiting a multi-modal machine learning engineer for its Trust & Safety team in San Jose. You will build and deploy models to protect users, collaborating with product and data science teams to define objectives and drive safety strategies at scale.

Ideal candidates have experience in ML (CV/NLP/Multimodal), strong Python skills, and a passion for solving large-scale safety problems, with a focus on online model workflows and deployment.