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Open nowPosted today

LLM Evaluation Engineer

MyCareersFuture95,505 open roles

Pay
SGD 6,000 – SGD 12,000 a month
Where
Central, Singapore
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Your applicationOpen nowLLM Evaluation EngineerMyCareersFuture · Central, Singapore

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The clock on this job

Early applications get read.

10.1% of postings close within 7 days. Measured by our own scanner across the market. MyCareersFuture postings stay open a median of 8 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.0%3 days
  3. 10.1%7 days
  4. 15.8%14 days
  5. 34.0%30 days
This job: posted today

MyCareersFuture median: 8 days open

The posting

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.
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