Senior Research Scientist/Software Engineer, LLM/Agent Platform (TikTok-Content Ecology AI Innovation & Platform)

Quick Facts

• Base salary range (annual): $254,400 - $588,000

Description

Research Scientists / Architects will drive AI innovations in LLM/Agent platforms by architecting scalable infrastructure and building novel LLM/Agent frameworks with self-improving capabilities.

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.

Requirements

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)

Benefits

• Day one access to medical, dental, and vision insurance
• 401(k) savings plan with company match
• Paid parental leave
• Short-term and long-term disability coverage
• Life insurance
• Wellbeing benefits
• 10 paid holidays per year
• 10 paid sick days per year
• 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure)

Eligibility for additional discretionary bonuses/incentives and restricted stock units.