Research Scientist Intern (Ads Integrity) - 2026 Start (PhD)

vely contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts.

Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).

Summer Start Dates:
May 11th, 2026
May 18th, 2026
May 26th, 2026
June 8th, 2026
June 22nd, 2026

Responsibilities:

1. Lead research and development of advanced generative AI technologies, including LLMs, multimodal models (text/image/video), and deepfake detection/synthesis, focusing on optimizing performance across pre-training, SFT, RLHF, and AI safety.
2. Design and deploy cutting-edge AIGC solutions for content understanding and monetization in diverse applications such as ads, e-commerce, short video, and live streaming, contributing to the creation of next-generation AI-driven ecosystems.
3. Drive advancements in LLM-based agents using reinforcement learning to enable autonomous reasoning, planning, and interactive capabilities, addressing real-world challenges in dynamic environments.
4. Innovate techniques to improve the efficiency of large-scale model training and inference, including distillation, quantization, and speculative decoding, for scalable and practical deployment in production.
5. Collaborate with interdisciplinary teams to transition research breakthroughs into production-grade AI services, ensuring robust, low-latency, and cost-effective solutions.
6. Stay at the forefront of generative AI research by contributing to patents, publications, and open-source projects, while actively monitoring and contributing to the latest industry trends and innovations.

Qualifications

Minimum Qualifications:

1. Current Ph.D. student in Computer Science, AI, Machine Learning, or related fields by 2026 (or equivalent industry experience).
2. Strong foundational experience in deep learning, NLP, and generative models (LLMs, diffusion models, etc.).
3. Hands-on experience with large-scale model training, RLHF (Reinforcement Learning from Human Feedback), and multimodal learning (text, image, video).
4. Proficiency in one or more deep learning frameworks such as PyTorch, JAX, or TensorFlow, with familiarity in distributed training frameworks.

Preferred Qualifications:

1. A track record of publications or active research/paper review at top-tier conferences (NeurIPS, ICML, ACL, CVPR, etc.) or equivalent.
2. Knowledge of AI safety, alignment, and adversarial robustness, with an interest in responsible AI development.
3. Experience in developing agentic systems utilizing reinforcement learning.
4. Strong engineering skills with the ability to deploy models at scale and optimize for performance.

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