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Senior Evaluation Algorithm Engineer
Binance
Remote, AUS
Full-time
Senior
Remote
Discovered Today
Python
Free
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Responsibilities
- Design end-to-end LLM evaluation plans for business scenarios such as dialogue and financial trading. Build evaluation metric systems and rubrics, transforming subjective model performance judgments into quantifiable, reproducible, and explainable evaluation conclusions.
- Lead the design and construction of evaluation datasets. Define evaluation dimensions and scenario coverage, establish high-quality data annotation guidelines and quality control processes, and build benchmarks that authentically reflect business needs and have discriminative power.
- Analyze model capability boundaries and failure modes based on evaluation results. Produce actionable improvement recommendations and collaborate with algorithm and product teams to drive model iteration, making evaluation a critical component of the R&D loop.
- Drive the automation and scaling of evaluation workflows. Build sustainable evaluation platforms and toolchains to support high-frequency, stable evaluation needs during rapid model iteration.
- Collaborate with algorithm, product, and data teams to translate business and model objectives into clear evaluation standards, and turn evaluation findings into concrete R&D directions and drive their implementation.
Requirements
- Master's degree or above in Computer Science, Artificial Intelligence, Mathematics, Statistics, or related fields, with a solid algorithmic foundation and understanding of LLM principles, training, and fine-tuning processes.
- Hands-on LLM evaluation experience at a large tech company, with participation in commercial deployment evaluation (not purely academic or offline benchmarking). Familiar with the full pipeline from evaluation data preparation and rubrics design to evaluation-driven R&D.
- Familiar with mainstream evaluation methods (human evaluation, model-based automatic evaluation / LLM-as-a-judge, metric computation) and their applicable boundaries. Able to define appropriate evaluation dimensions for different business scenarios and write clear, actionable, and discriminative rubrics.
- Systematic control over evaluation data representativeness, annotation consistency, and result reliability, ensuring scientific and trustworthy evaluation conclusions.
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