Job description

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 Qualifications - BS/BA or Master in Computer Science or related technical field or equivalent technical experience - 5+ 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 Qualifications: - 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).

Requirements

Must-haves

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.
BS/BA or Master in Computer Science or related technical field or equivalent technical experience
5+ 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.
5+ years of experience as an architect, or technical leadership position

Nice-to-haves

PhD in Computer Science or related technical discipline.
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).

Application prep

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Keywords to mirror

aillmmachine learningml

Platform: tiktok
Effort: medium

Benefits

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