About the job
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.
Responsibilities
Research and develop cutting-edge generative AI technologies, including LLMs, multimodal models (text/image/video), and deepfake detection/synthesis, optimizing performance across pre-training, SFT, RLHF, and AI safety.
Design and deploy AIGC solutions for content understanding and monetization in ads, e-commerce, short video, and live streaming, building next-gen AI-driven ecosystems.
Advance LLM-based agents with reinforcement learning, enabling autonomous reasoning, planning, and interactive capabilities for real-world applications.
Improve efficiency of large-scale model training and inference, exploring techniques like distillation, quantization, and speculative decoding for scalable deployment.
Collaborate cross-functionally to productionized research breakthroughs, ensuring robustness, low-latency, and cost-effective AI services.
Stay ahead of industry trends, contributing to patents, publications, and open-source projects in generative AI.
Qualifications
Minimum
MS or PhD in Computer Science, AI, Machine Learning, or related fields (or equivalent industry experience)
Solid transferrable experience in deep learning, NLP, and generative models (LLMs, diffusion models, etc.)
Hands-on experience with large-scale model training, RLHF, and multimodal learning
Proficiency in PyTorch, JAX, or TensorFlow, and familiarity with distributed training frameworks
Preferred
Knowledge of AI safety, alignment, and adversarial robustness
Experience in building agentic systems with reinforcement learning.
Strong engineering skills for deploying models at scale.