- Paya Lebar Road Geylang Southeast (Singapore) Singapore
Full Time Large Language Model (LLM) Evaluation Engineer Jobs, Salary up to SGD 12,000 in KUAILU SOFTWARE (SINGAPORE) PTE. LTD. Southeast (Singapore) - Maukerja
Large Language Model (LLM) Evaluation Engineer
KUAILU SOFTWARE (SINGAPORE) PTE. LTD.
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Job Description
Responsibilities
Job Responsibilities
Build and maintain an automated LLM evaluation pipeline covering multiple dimensions, including general capabilities, Agent capabilities, and persona/role-playing. The pipeline should support one-click evaluation, historical result comparison, and regression testing.
Conduct general capability evaluations using 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 metrics for benchmarks such as BFCL, τ-bench, and GAIA.
Design and execute persona/role-playing evaluation frameworks, covering metrics such as identity recognition, role compatibility, multi-turn stability, and style consistency.
Record and analyze evaluation results from training runs, conduct comparative analysis and anomaly detection, and produce checkpoint evaluation reports.
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 field.
Familiarity with mainstream LLM evaluation benchmarks and frameworks, such as lm-eval-harness, OpenCompass, and EvalPlus.
Strong proficiency in Python, with the ability to independently build evaluation pipelines covering model inference/deployment, batch evaluation, and results analysis.
Familiarity 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 visualization.
Detail-oriented and rigorous, with a strong focus on ensuring the reproducibility and reliability of evaluation results.
Preferred Qualifications
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 with evaluation automation and CI/CD integration.
Understanding of model training workflows, with the ability to understand the relationship between training checkpoints and evaluation results.
Kuailu Technology is dedicated to building a one-stop AI-powered intelligent office system. With a low-code platform as its foundation and AI agents as the engine, it integrates a full-scenario office ecosystem encompassing AI-driven office work, collaboration, and meetings. The system covers all business scenarios, including HR, finance, procurement, administration, and CRM. By establishing a unified data foundation to replace multiple software systems, it breaks down internal information silos, enables seamless data flow across entire business processes, and activates core data assets related to people, capital, and materials. This transforms data into intelligent productivity, helping enterprises achieve low-cost, high-efficiency, and integrated smart office operations.
Build and maintain an automated LLM evaluation pipeline covering multiple dimensions, including general capabilities, Agent capabilities, and persona/role-playing. The pipeline should support one-click evaluation, historical result comparison, and regression testing.
Conduct general capability evaluations using 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 metrics for benchmarks such as BFCL, τ-bench, and GAIA.
Design and execute persona/role-playing evaluation frameworks, covering metrics such as identity recognition, role compatibility, multi-turn stability, and style consistency.
Record and analyze evaluation results from training runs, conduct comparative analysis and anomaly detection, and produce checkpoint evaluation reports.
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 field.
Familiarity with mainstream LLM evaluation benchmarks and frameworks, such as lm-eval-harness, OpenCompass, and EvalPlus.
Strong proficiency in Python, with the ability to independently build evaluation pipelines covering model inference/deployment, batch evaluation, and results analysis.
Familiarity 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 visualization.
Detail-oriented and rigorous, with a strong focus on ensuring the reproducibility and reliability of evaluation results.
Preferred Qualifications
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 with evaluation automation and CI/CD integration.
Understanding of model training workflows, with the ability to understand the relationship between training checkpoints and evaluation results.
Kuailu Technology is dedicated to building a one-stop AI-powered intelligent office system. With a low-code platform as its foundation and AI agents as the engine, it integrates a full-scenario office ecosystem encompassing AI-driven office work, collaboration, and meetings. The system covers all business scenarios, including HR, finance, procurement, administration, and CRM. By establishing a unified data foundation to replace multiple software systems, it breaks down internal information silos, enables seamless data flow across entire business processes, and activates core data assets related to people, capital, and materials. This transforms data into intelligent productivity, helping enterprises achieve low-cost, high-efficiency, and integrated smart office operations.
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