Our work spans three layers:

Standards & Governance
We define and iterate the safety standards that AI systems must follow, translating complex policy intent into structured, machine-interpretable frameworks.

This requires deep governance thinking:

navigating trade-offs between safety, fairness, user experience, and enforcement consistency.

AI/ML Solution Design
We partner closely with algorithm teams to improve model accuracy and stability across safety scenarios, tackling challenges unique to this domain
adversarial content, imbalanced distributions, and deep contextual understanding. We evaluate, select, and help shape the right AI approaches (LLMs, prompting strategies, agentic workflows, etc.) for each problem.
Rigorous Evaluation
We design statistically grounded evaluation frameworks, build high-quality ground truth datasets, and ensure our assessments are valid, reproducible, and actionable
so the platform can confidently ship AI-powered safety systems at scale. Our work sits at the intersection of AI/ML product development, trust & safety policy, and data-driven quality assurance
ensuring that AI systems can be reliably deployed for high-precision content review and risk governance at scale.

We are looking for talented individuals to join us for an internship. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth. Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals. Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted. Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates. Responsibilities

Design and execute evaluation plans for AI safety models
define evaluation objectives, select appropriate metrics, and determine what \"good\" looks like for each use case.
Build and maintain high-quality ground truth datasets
design data sampling strategies, develop data cleaning pipelines, and ensure labeling consistency and accuracy.
Analyze model performance using statistical methods (sampling design, confidence intervals, error analysis) to produce actionable insights for algorithm teams and stakeholders.
Collaborate with algorithm engineers to translate evaluation findings into concrete model improvement directions; participate in prompt design and model configuration iteration.
Communicate evaluation results and governance standards to cross-functional partners (Policy, Operations, Algorithm); align on definitions and help calibrate quality expectations.
Continuously improve evaluation processes
identify gaps, propose methodology upgrades, and ensure our evaluation systems scale with model and policy evolution. Qualifications Minimum Qualifications
Currently pursuing an Undergraduate/Master's in Statistics, Computer Science, Data Science, Public Policy, or closely related quantitative fields.
Solid grasp of applied statistics
sampling, hypothesis testing, confidence intervals, distribution analysis
and ability to apply these to real measurement problems.
Foundational understanding of AI/ML concepts (classification, NLP, LLMs, precision/recall); comfortable discussing model behavior with engineers.
Interest in governance, policy, or content safety; appreciation for the complexity of defining \"right\" and \"wrong\" at scale.
Strong structured thinking
able to decompose ambiguous problems into clear goals and prioritized actions; goal-oriented and hypothesis-driven.
Ability to communicate in both English and Chinese to collaborate with global and China-based stakeholders. Preferred Qualifications
Internship or project experience in Trust & Safety, AI/ML product, model evaluation, or policy-related work.
Hands-on experience with prompt engineering, LLM-based evaluation, or building evaluation datasets.
Familiarity with safety-specific challenges: adversarial content, inter-annotator disagreement, or human-in-the-loop systems.
Coursework or research in AI governance, computational social science, or interdisciplinary areas combining technology and policy.
Experience with experimental design and statistical modeling beyond introductory level.

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