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AI Agentic Research Lead Project Intern; Seed-Global Data PhD

Job in Los Angeles, Los Angeles County, California, 90009, USA
Listing for: ByteDance
Full Time, Apprenticeship/Internship position
Listed on 2026-02-17
Job specializations:
  • Research/Development
    Data Scientist, Research Scientist
Job Description & How to Apply Below
Position: AI Agentic Research Lead Project Intern (Seed-Global Data) - 2026 Start (PhD)
About the team As a core member of our Seed Global Data Team, you'll be at the heart of our operations. Gain first-hand experience in understanding the intricacies of training Large Language Models (LLMs) with diverse data sets. Through our carefully designed rotation program, you'll witness how high-quality data is meticulously crafted and used. As a key member of our Seed Global Data Team, the AI Agentic Research Lead project intern will play a pivotal role in leading end-to-end research initiatives focused on next-generation Agentic LLM systems.

This role demands deep technical ownership-from defining the problem space and designing rigorous experiments to analyzing data and driving iterative improvements. You will serve as the bridge between theoretical research and practical application, collaborating closely with cross-functional teams in China to deliver statistically significant breakthroughs in agent capabilities. As a project intern, you will have the opportunity to engage in impactful short-term projects that provide you with a glimpse of professional real-world experience.

You will gain practical skills through on-the-job learning in a fast-paced work environment and develop a deeper understanding of your career interests. Applications will be reviewed on a rolling basis - we encourage you to apply early. Successful candidates must be able to commit to at least 3 months long internship period. Your Role Will Involve:
1. Drive the full research lifecycle, including scoping research questions, designing experimental protocols, overseeing data collection, performing deep statistical analysis, and iterating on model architectures or prompting strategies.
2. Actively collaborate with engineering and research teams in China. This requires fluent Chinese proficiency to ensure seamless technical communication, alignment on goals, and rapid knowledge transfer across time zones.
3. Provide technical guidance and mentorship to Research Specialists, ensuring experimental rigor and code quality while fostering a collaborative research environment.
4. Regularly synthesize research progress, blockers, and insights into high-quality technical reports and presentations for PMs and research leadership.

Minimum Qualifications 1. Currently pursuing or recently completed a PhD or Post-Doc in Computer Science, Electrical Engineering, Statistics, or a related field. 2. 3+ years of research experience in LLMs, AI, or Machine Learning, with a portfolio of completed research projects.
3. Expert proficiency in Python, particularly with data analysis and scientific computing libraries (pandas, numpy, scipy, scikit-learn). Strong command of experimental design and statistical analysis.
4. Hands-on experience with prompt engineering, agentic frameworks (e.g., Lang Chain, AutoGPT), and LLM evaluation methodologies.
5. Fluent proficiency in Mandarin is strictly required for collaboration with global teams as there will be close communication with Mandarin-speaking global research team on agentic model training and training data analysis.

Preferred Qualifications:

1. First-author publications in top-tier AI/ML conferences (NeurIPS, ICML, ICLR, etc.). 2. Direct experience with RLHF (Reinforcement Learning from Human Feedback) or RLAIF workflows.
3. Familiarity with distributed training infrastructure and large-scale data visualization/charting tools. Work Arrangement:
Full-time (10-24 weeks). Onsite preference for maximal lab collaboration; hybrid arrangements considered for exceptional candidates. By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here:
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