Research Engineer Graduate

Responsibilities

Our Business Integrity team has a strong user focus and a dedication to technical excellence. We aim to meet our users’ needs with reliable and high-performing platforms and services. We are looking for strong machine learning engineers who are excited to grow their business understanding, build highly scalable machine learning models, and partner across disciplines with global teams, in pursuit of excellence. Given the fast growth of TikTok in the world, we are working on building a next-generation content understanding system for TikTok monetization. We are seeking Research Engineers who are experienced in machine learning, which can help us create an ecosystem that rewards high-quality user experience and advertiser value. Topic Content: With the explosive growth of digital content, intelligent moderation has become a core capability for internet platforms. However, as moderation scenarios grow increasingly complex and adversarial tactics continue to evolve, traditional approaches are facing unprecedented challenges. The current landscape is characterized by multiple technical difficulties, including the dynamic nature of moderation rules, content complexity, sample scarcity, escalating adversarial behaviors, and a lack of interpretability. In particular, existing open-source large models often do not perform as effective as we expect in scenarios involving evolving moderation rules, long-form text, long temporal sequences, multi-languages, limited sample data, and adversarial content generated by AIGC. We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Responsibilities: In this role, you will build a leading moderation system that enables end-to-end capabilities for accurate rejection decisions, interpretable reasoning, and intelligent remediation, achieving fully automated moderation with performance surpassing human benchmarks.

Qualifications

Minimum Qualifications: - Individuals who are completing or have recently completed a PhD degree in Computer Science, Software Engineering, Electronic Engineering, Automation, Mathematics, Statistics, or a related technical discipline (completed, or expecting to graduate within 12 months). - Hands-on experience implementing, training, or deploying models in one or more of the following areas — Ads, Search, Recommender Systems, NLP, CV, Multimodal, or Agent technologies — through coursework, internships, or industry projects. - Familiarity with the fundamentals of large language models (e.g. pre-training, fine-tuning, prompting, inference and deployment), with practical experience in at least one of these stages. - Strong software engineering fundamentals: clean, testable code, version control, and the ability to debug complex systems end to end. - Proficient in Python and at least one deep learning framework such as PyTorch or TensorFlow. Strong problem-solving ability, a collaborative mindset, and a genuine interest in applying AI to real-world products. Preferred Qualifications: - Experience with large-scale distributed training (DeepSpeed, Megatron, FSDP) or high-throughput inference (vLLM, SGLang, TensorRT-LLM). - Experience with production model deployment, serving optimization, or latency/cost tuning. - Experience building data pipelines for large-scale training or evaluation. - Experience building agentic systems, RAG pipelines, or multimodal applications. - Familiarity with the ads / search / recommendation industry stack. - Open-source contributions, strong results in ML competitions (Kaggle, Tianchi, ACM/ICPC), or granted patents.

Job Information