The posting
Job Description:
- Design and own the evaluation system for LLM Agents, including benchmark construction, LLM-as-Judge with human annotation, and regression/comparison pipelines. Continuously monitor product metrics to guide Agent and model optimization.
- Build Agent reliability analysis, including error attribution and uncertainty estimation across multi-step trajectories, and turn failure cases into targeted improvements.
- Drive evaluation-guided Agent optimization, including Harness design (planning, verification, self-refine), tool calling, context engineering, and memory.
- Lead evaluation-driven post-training of LLMs for vertical domains (SFT, DPO, RL), including data pipelines and reward model design.
- Build and optimize RAG/retrieval systems and the Agent tool layer (search, code interpreters, sandboxes, MCP) and evaluate their impact on end-to-end Agent quality.
- Explore frontier research in Agent reliability and evaluation and drive a full closed loop from research to deployment.
Requirements:
- Master’s degree or above in Artificial Intelligence, Computer Science, Mathematics, or a related field.
- At least 1 year of full-time industry experience building and deploying production LLM Agent systems (task planning, orchestration, tool calling), including designing evaluation sets and metrics (LLM-as-Judge, human annotation, error attribution) to drive iteration.
- Hands-on experience fine-tuning LLMs via SFT and DPO (GRPO/RL a plus), and building and optimizing RAG/retrieval systems (recall, ranking, embedding fine-tuning).
- Good programming skills; proficient in Python (SQL and PyTorch are a plus). Familiar with training and inference frameworks such as verl, vLLM, FSDP, or LLaMA-Factory.
- Strong analytical skills, with a solid grasp of statistics and experiment design. Sustained curiosity about frontier AI, strong self-drive, and the ability to collaborate closely with teams to drive a full closed loop from research to deployment.
