腾讯Agent Evaluation Intern 107491
伦敦 · 社招 · 工程与基础设施
需要对标的能力
大模型原理 · LLM 基础Prompt Engineering · 提示词工程Agent · 智能体设计工具调用 · Function Calling上下文工程 · 记忆管理Agent 工程 · Harness / 脚手架成本优化 · 性能调优评测 · Eval 方法论AI 安全 · Prompt 注入防御
岗位职责与要求
About the Hiring Team
Level Infinite is Tencent’s global gaming brand. It is a global game publisher offering a comprehensive network of services for games, development teams, and studios around the world.
We are dedicated to delivering engaging and original gaming experiences to a worldwide audience, whenever and wherever they choose to play while building a community that fosters inclusivity, connection, and accessibility. Level Infinite also provides a wide range of services and resources to our network of developers and partner studios around the world to help them unlock the true potential of their games.
What the Role Entails
We are hiring an intern to work on evaluation and reliability infrastructure for a real-world LLM agent system in the UA performance marketing field. The agent performs multi-step reasoning, retrieves context, selects tools, executes actions, handles user confirmations, and interacts with external services.
The goal of this internship is to build transferable expertise in agent evaluation engineering: evaluating tool use, measuring trajectory quality, designing benchmarks, analyzing traces, comparing model and prompt variants, and improving the reliability of agentic AI systems.
This role is ideal for someone interested in future opportunities in LLM agent evaluation, AI safety evaluation, research engineering, LLMOps, or applied AI infrastructure.Research the state-of-the-art agentic workflow evaluation frameworks in the industry and in the research field.
Apply the theory to build automated evaluation pipelines that can run agent scenarios, capture execution artifacts, score results, and detect regressions.
Evaluate tool-use behavior, including whether the agent selects the right tool, passes correct arguments, avoids unnecessary calls, and handles tool errors appropriately.
Analyze agent trajectories using traces, logs, intermediate steps, and final outputs to identify reasoning failures, context misuse, hallucinated assumptions, and brittle workflow patterns.
Design metrics for agent reliability, including success rate, tool-call precision, argument accuracy, recovery rate, retry count, latency, cost, and safety-related failure rates.
Create reusable evaluation datasets from synthetic cases, golden workflows, and real anonymized executions.
Support experiments comparing prompts, model providers, tool descriptions, memory strategies, context construction methods, and execution modes.
Help build human evaluation workflows and rubrics for judging agent correctness, faithfulness, usefulness, and risk awareness.
Work with engineers to translate evaluation findings into better tests, monitoring signals, tool interfaces, prompts, and guardrails.
Potentially compose research papers and publish in scientific conferences.
Who We Look For
Currently pursuing or recent graduates of a Master’s or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Data Science, or a related field.
Strong Python fundamentals and interest in AI systems.
Curious about how LLM agents work, fail, and improve.
Interested in evaluation methodology, not just application building.
Comfortable reading logs, traces, test cases, and structured data.
Detail-oriented and able to define clear, measurable criteria for ambiguous agent behavior.
Prior experience with LLMs, LangChain-like agents, tool calling, pytest, data analysis, or observability tools is helpful but not required.
Equal Employment Opportunity at Tencent
As an equal opportunity employer, we firmly believe that diverse voices fuel our innovation and allow us to better serve our users and the community. We foster an environment where every employee of Tencent feels supported and inspired to achieve individual and common goals.
Work Location: UK-London
岗位要求详情请见上方岗位职责内说明
查看中文参考译文
关于招聘团队
Level Infinite 是 Tencent 的全球游戏品牌。它是一家全球性游戏发行商，为全球的游戏、开发团队和工作室提供全面的服务网络。
我们致力于为全球玩家提供引人入胜且原创的游戏体验，无论他们何时何地选择游玩，同时构建一个促进包容性、连接性和可及性的社区。Level Infinite 还为其全球开发者和合作工作室网络提供广泛的服务和资源，帮助他们释放游戏的真正潜力。
岗位职责
我们正在招聘一名实习生，为 UA 效果营销领域的真实世界 LLM agent 系统构建 evaluation 和 reliability infrastructure。该 agent 执行多步推理、检索上下文、选择工具、执行操作、处理用户确认，并与外部服务交互。
本次实习的目标是培养 agent evaluation engineering 方面的可迁移专业能力：评估 tool use、衡量 trajectory quality、设计 benchmark、分析 trace、比较 model 和 prompt 变体，以及提升 agentic AI 系统的可靠性。
该岗位适合对以下领域未来机会感兴趣的人士：LLM agent evaluation、AI safety evaluation、research engineering、LLMOps，或 applied AI infrastructure。研究业界和研究领域中最先进的 agentic workflow evaluation 框架。
将理论应用于构建 automated evaluation pipeline，能够运行 agent scenario、捕获执行产物、评分结果并检测 regression。
评估 tool-use 行为，包括 agent 是否选择正确的工具、传递正确的参数、避免不必要的调用，以及适当地处理 tool error。
使用 trace、log、intermediate step 和最终输出分析 agent trajectory，以识别 reasoning failure、context misuse、hallucinated assumption 和脆弱的工作流模式。
为 agent reliability 设计指标，包括 success rate、tool-call precision、argument accuracy、recovery rate、retry count、latency、cost 以及 safety-related failure rate。
从 synthetic case、golden workflow 和真实匿名化执行中创建可复用的 evaluation dataset。
支持对比 prompt、model provider、tool description、memory strategy、context construction method 和 execution mode 的实验。
协助构建 human evaluation workflow 和 rubric，用于评判 agent 的 correctness、faithfulness、usefulness 和 risk awareness。
与工程师合作，将 evaluation 发现转化为更好的 test、monitoring signal、tool interface、prompt 和 guardrail。
可能撰写研究论文并在科学会议上发表。
我们寻找的人才
目前正在攻读或新近获得 Computer Science、Artificial Intelligence、Machine Learning、Software Engineering、Data Science 或相关领域的 Master's 或 PhD 学位。
扎实的 Python 基础，并对 AI 系统感兴趣。
对 LLM agent 如何工作、失败和改进充满好奇。
对 evaluation methodology 感兴趣，而不仅仅是应用构建。
能够熟练阅读 log、trace、test case 和结构化数据。
注重细节，能够为模糊的 agent 行为定义清晰、可衡量的标准。
有 LLM、LangChain-like agent、tool calling、pytest、data analysis 或 observability tool 方面的经验者优先，但非必需。
Tencent 的平等雇佣机会
作为平等雇佣雇主，我们坚信多元的声音推动创新，让我们更好地服务用户和社区。我们营造一种环境，让 Tencent 的每位员工都感到受到支持，并有动力去实现个人和共同的目标。
工作地点：英国-伦敦
译文仅供辅助阅读，具体要求以原始招聘来源为准。