TikTok
Senior Research Scientist/AI Software Engineer, LLM/Agent Platform (TikTok-Content Ecology AI Innovation & Platform) - TikTok Content Ecology
San Jose, California · Senior · Full-time · Posted Oct 28, 2026
- Experience
- 5+ years of hands-on software engineering experience; preferred: 5+ years as an architect or in a technical leadership position.
- Education
- BS/BA or Master in Computer Science or related technical field, or equivalent technical experience. PhD preferred.
About the job
Builds scalable infrastructure and agent frameworks for the lifecycle of large language models and AI agents, supporting research and deployment across TikTok’s global content ecosystem.
What you'll do
- Build reusable pipelines for model and agent data processing, training, deployment, monitoring, and governance.
- Develop platform infrastructure, observability, evaluation, and retrieval systems in collaboration with algorithm teams.
- Design and optimize agentic AI systems, including planning, tool use, and memory.
What they ask for
- BS/BA or Master in Computer Science or related technical field, or equivalent technical experience.
- 5+ years of hands-on software engineering experience focused on machine learning, distributed systems, or AI infrastructure.
- Strong proficiency integrating AI tools into knowledge discovery and research workflows, and familiarity with evaluation frameworks and experimental reproducibility.
- Expertise in deep learning frameworks and tensor libraries such as PyTorch, Tensorflow, JAX/FLAX; solid understanding of machine learning fundamentals and the modern AI stack.
Nice to have
- PhD in Computer Science or related technical discipline; 5+ years as an architect or in technical leadership.
- Experience with ML infrastructure, large-scale model training and inference, agent architectures, or publications in systems and/or machine learning conferences.
Tools
PyTorch, Tensorflow, JAX/FLAX, Triton, TensorRT-LLM, FAISS, Milvus, DeepSpeed/ZeRO
AI focus
Builds LLM and agent platform infrastructure, including self-improving agent frameworks, model lifecycle pipelines, evaluation, retrieval/RAG, and agent planning, tool use, and memory.
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