Responsibilities
- Work on fundamental problems in LLM-based agentic systems and efficient AI infrastructure, with opportunities to publish your research while making direct impact on production systems serving enterprise customers.
- Conduct original research on LLM agent architectures and optimization techniques
- Develop and evaluate novel algorithms with both academic rigor and production feasibility
- Present your work at internal research seminars and external conferences
- Mentor and collaborate with LLM engineers on implementation and deployment
Requirements
- Currently pursuing MS/PhD in Computer Science, Machine Learning, Natural Language Processing, or related fields
- Publications at top-tier venues (ICML, NeurIPS, ICLR, ACL, EMNLP, NAACL)
- Strong programming skills in Python and PyTorch
- Ability to work in-person at our San Francisco office
- Ability to work independently and collaborate across research and engineering teams
Nice to Have
- Experience with self-evolving agent systems
- Proficiency in CUDA programming and custom kernel development for LLM operations
- Background in reinforcement learning-based LLM fine-tuning
- Track record of contributions to production inference systems such as vLLM, TensorRT-LLM, SGLang, or Hugging Face ecosystem
- Experience bridging academic research with production systems
- Open-source contributions to widely-used ML infrastructure projects
