Senior Evaluation Algorithm Engineer

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products.

Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world.

About the Role

In the AI era, large language models are reshaping core business scenarios such as dialogue and trading. Model capability iteration relies on a scientific and trustworthy evaluation system — the "ruler" that measures model quality and guides R&D direction. We are seeking an evaluation expert with an algorithmic background to build LLM evaluation capabilities covering dialogue, financial trading, and other scenarios, using professional evaluation methods to quantify model performance, pinpoint issues, and drive continuous model improvement.

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. Proficient in Python, with experience in evaluation workflow automation, benchmark construction, or evaluation platform development.

Able to independently handle data processing, evaluation script writing, and result analysis. Strong business understanding and communication skills, able to translate evaluation findings into clear improvement directions and effectively drive cross-team collaboration. Bonus

Qualifications

Experience evaluating dialogue systems, AI Agents, or financial/trading LLMs. Experience building high-quality AI training/evaluation data or data annotation systems. Familiarity with RLHF, reward models, or preference data-related work.