Research Scientist Intern (Data-TnS-Algo-Foundations & Intelligence Service)- 2026 Start (PhD) job opportunity at Tiktok.

Home Jobs In United States Of America Tag: Research Scientists Research Scientist Intern (Data-TnS-Algo-Foundations & Intelligence Service)- 2026 Start (PhD) position at Tiktok


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Tiktok Research Scientist Intern (Data-TnS-Algo-Foundations & Intelligence Service)- 2026 Start (PhD)
Experience: 2-years
Pattern: full-time
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Research Scientists

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degreePhD
loacation SAN JOSE, CA, United States Of America
loacation SAN JOSE, CA....United States Of America

Responsibilities DayDreamer (Foundation Models, #Trust & Safety) is TikTok’s foundation-model team responsible for core development and research that push the boundaries of our #LLM and VLM capabilities—with native trustworthiness and safety built in from day one. Our work enables product teams and customers to adopt advanced #AI smoothly and responsibly, with safety woven into the model stack rather than bolted on. We are looking for talented individuals to join us for an internship in 2026. Internships at TikTok aim to offer students industry exposure and hands-on experience. Turn your ambitions into reality as your inspiration brings infinite opportunities at TikTok. PhD internships at TikTok provide students with the opportunity to actively 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). Responsibilities: Pretraining & Continued Pretraining (CPT): Explore and develop approaches that improve general capability and safety for LLMs and VLMs via pretraining/CPT. Evaluation & Measurement: Build advanced evaluation systems to study emerging LLM/VLM skills and safety behaviors, aligned with real downstream use. Post-Training & RL: Develop advanced reinforcement-learning strategies (e.g., RLHF/RLAIF/DPO variants), and investigate how to balance pretraining and post-training for better capability and safety alignment. Scaffolded Settings & Agents: Probe new failure modes and pitfalls in scaffolded environments (agents, workflows, tool use), and translate insights into robust mitigations.

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