The role
From TikTok's own posting.
The Content Ecology Algorithm Team drives TikTok’s AI innovations in LLMs, NLP, Computer Vision (CV), multimodal learning, and recommendation algorithms. We develop cutting-edge AI capabilities that power multiple business lines.
We are seeking exceptional, experienced Research Scientists / Architects in the areas of LLM / Agent Platform. This is a unique role for innovators who are passionate about building the "bones" (scalable infrastructure) of next-generation AI and Agent. You will design and implement novel LLM/Agent frameworks with self-improving capabilities and architect the robust, high-performance infrastructure that brings these LLMs and agents to life at a global scale. This is an opportunity to shape the future of AI at one of the world's most dynamic technology companies.
What You’ll Do
- Architect and build standardized, configurable, and reusable pipelines for the entire lifecycle of models and agents—from data processing and training to deployment, monitoring, and governance.
- Partner with algorithm teams to understand their needs and provide a world-class infrastructure platform that accelerates their research and development cycles.
- Build robust observability and evaluation frameworks to ensure the reproducibility, reliability, and cost-efficiency of AI workloads at scale.
- Design and implement core platform infrastructure, including model/agent registries, feature stores, and high-throughput retrieval/RAG systems.
- Design, build, and optimize advanced Agentic AI systems, focusing on core components like planning, tool use, and memory.
Minimum Qualification(s):
- BS/BA or Master's in Computer Science or related technical field or equivalent technical experience
- 2+ years of hands-on experience in software engineering, with a focus on machine learning, distributed systems, or AI infrastructure.
- Strong proficiency in integrating AI tools into knowledge discovery and research workflows.
- Familiarity with building robust evaluation frameworks and ensuring experimental reproducibility.
- Expertise in deep learning frameworks and tensor libraries like PyTorch, Tensorflow, JAX/FLAX
- Solid understanding of machine learning fundamentals and the modern AI stack.
- Excellent communication skills to collaborate across teams.
Preferred Qualification(s):
- PhD in Computer Science or related technical discipline.
- 5+ years of experience as an architect, or technical leadership position
- Experience with the ML infrastructure ecosystem, including GPU scheduling, model serving (Triton, TensorRT-LLM), vector databases (FAISS, Milvus), and MLOps principles.
- Experience with large-scale model training and inference, including distributed training, KV cache–aware serving, GPU/accelerator optimization, and high-performance networking (e.g., RDMA, NCCL).
- Experience with performance optimization of large model training and inference (e.g., DeepSpeed/ZeRO, vLLM).
- Deep knowledge of agent architectures, including planning, tool use (e.g., LangChain, LlamaIndex), and memory systems.
- Publications in systems and/or machine learning conferences (e.g., NeurIPS, OSDI, SOSP, ASPLOS, MLSys).