Job description

About the Team Our team is at the forefront of building and optimizing content safety systems. With a focus on optimising and advancing content safety, we leverage advanced large language and multimodal models to enhance review efficiency, risk control, and user trust. Working closely with business and technical stakeholders, we deliver scalable solutions that keep pace with rapid global growth. As a Project Intern, you will contribute to impactful short-term projects and gain hands-on experience in a fast-paced, professional environment. This internship offers the opportunity to develop practical skills, apply your knowledge to real-world challenges, and explore your career interests. Applications are reviewed on a rolling basis, so we encourage you to apply early. Responsibilities - Assist in the optimization and revamp of the Human Moderation queue, supporting product design and execution across task allocation, priority management, processing efficiency, and moderator experience improvement in manual review workflows. - Assist in building and refining metric frameworks related to review efficiency, review quality, and operational collaboration; continuously monitor performance and support strategy iteration based on outcome evaluation. - Participate in the analysis, design, and iteration of LIVE Safety product features and workflows, contributing to stronger content safety governance capabilities and a more trustworthy user experience. - Identify risk points, workflow bottlenecks, and product opportunities within moderation systems through data analysis, case studies, and business feedback. Minimum Qualifications - Currently pursuing an Undergraduate in computer science, analytics, Information Technology/System or other related discipline. - Able to commit to working for 5 days a week for at least 6 months - Foundational understanding of safety, product management or product design, demonstrated through coursework, projects, or internships. - Basic technical proficiency with analytical tools and data sense gained through coursework, projects, or relevant experience. - Good communication skills, able to communicate with cross-functional teammates to clarify pipeline design or SOP training. Preferred Qualifications - Experience on Trust & Safety, moderation, LIVE or risk control - Exposure to AI/ML-driven detection or recommendation systems, including an understanding of their limitations in adversarial or real-time contexts.

Requirements

Must-haves

About the Team
Applications are reviewed on a rolling basis, so we encourage you to apply early.
Assist in building and refining metric frameworks related to review efficiency, review quality, and operational collaboration; continuously monitor performance and support strategy iteration based on outcome evaluation.
Participate in the analysis, design, and iteration of LIVE Safety product features and workflows, contributing to stronger content safety governance capabilities and a more trustworthy user experience.
Identify risk points, workflow bottlenecks, and product opportunities within moderation systems through data analysis, case studies, and business feedback.
Currently pursuing an Undergraduate in computer science, analytics, Information Technology/System or other related discipline.
Able to commit to working for 5 days a week for at least 6 months
Foundational understanding of safety, product management or product design, demonstrated through coursework, projects, or internships.
Basic technical proficiency with analytical tools and data sense gained through coursework, projects, or relevant experience.
Good communication skills, able to communicate with cross-functional teammates to clarify pipeline design or SOP training.

Nice-to-haves

Experience on Trust & Safety, moderation, LIVE or risk control
Exposure to AI/ML-driven detection or recommendation systems, including an understanding of their limitations in adversarial or real-time contexts.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiml

Platform: tiktok
Effort: medium