LLM Evaluation Engineer

Job Description

• Build and maintain automated LLM evaluation pipelines covering multiple dimensions, including general capabilities, Agent capabilities, and persona/role-playing capabilities. Support one-click evaluation, historical result comparison, and regression testing.
• Conduct general capability evaluations using established benchmarks such as MMLU, C-Eval, HumanEval, GSM8K, MATH, and IFEval, including benchmark deployment, execution, and results analysis.
• Conduct Agent capability evaluations, including setting up evaluation environments and tracking performance metrics for benchmarks such as BFCL, τ-bench, and GAIA.
• Design and execute persona and role-playing evaluation frameworks, covering metrics such as identity recognition, role consistency, multi-turn stability, and style consistency.
• Record and analyse evaluation results across model training runs, perform comparisons and anomaly detection, and prepare evaluation reports for training checkpoints.
• Conduct regular intermediate evaluations during the pre-training stage to track the evolution and improvement of model capabilities.

Job Requirements

• Bachelor's degree or above in Computer Science, Artificial Intelligence, or a related discipline.
• Familiar with mainstream LLM evaluation benchmarks and frameworks, such as lm-eval-harness, OpenCompass, and EvalPlus.
• Proficient in Python, with the ability to independently build evaluation pipelines covering model inference/deployment, batch evaluation, and results analysis.
• Familiar with LLM inference frameworks such as vLLM and SGLang, with the ability to deploy models for batch inference and evaluation.
• Experience in evaluation data analysis and visualisation.
• Detail-oriented and rigorous, with a strong focus on ensuring the reproducibility and reliability of evaluation results.

Good to Have

• Experience with Agent evaluation, particularly BFCL, τ-bench, GAIA, or SWE-bench.
• Experience with persona or role-playing evaluation, such as CharacterBench or RMTBench.
• Experience in evaluation automation and CI/CD integration.
• Understanding of LLM training workflows, with the ability to interpret the relationship between training checkpoints and evaluation results.