关于我
About
目前在镁佳科技担任智能座舱产品经理,围绕地图与语音 Agent,将出行场景转化为座舱产品设计。
此前在猎豹移动担任AI 产品经理,参与 EasyClaw 工作流、自动运营系统和社区设计,将用户场景与反馈带入产品优化。
约克大学(英国)人机交互 硕士背景,结合 CDS 车辆与座舱设计训练,关注从用户研究到车内交互体验的连接。
At Megatronix, I work as a Digital Cockpit Product Manager, translating mobility scenarios into navigation and voice Agent experiences.
Previously an AI Product Manager at Cheetah Mobile, I worked on EasyClaw workflows, automated operations and community design, connecting user scenarios and feedback with product improvements.
My Master's in Human-Computer Interaction background at University of York, UK, together with CDS vehicle and cockpit design training, connects user research with in-car interaction design.
工作 & 项目
Experience
- 7 款全球车型座舱 AI 产品规划:负责长安深蓝 SL03、S05、L06、L07、S07、S09、马自达 EZ6(6e) 的平台化智能座舱产品 UE 定义及设计,主要负责地图 Agent 和语音 Agent 模块
- 地图 Agent 场景设计:聚焦导航、POI、路线规划与出行决策场景,优化 50+ 项多指令意图、新增 20+ 个场景模糊指令意图识别,与 NLU 团队共同从传统搜索导航升级至导航 Agent 优化使用体验,使用户意图识别率提高约 135%
- 语音 Agent 体验优化:分析车内语音指令、多轮对话、任务中断与误识别等 100+ 体验问题,通过新增端侧语音功能点的形式,突破传统大模型速度限制,提高用户意图识别率,共 20+ 任务意图的平均语音响应时间降至约 20%
- Cockpit AI planning for seven global vehicle models: defined and designed platform-level cockpit UE for DEEPAL SL03, S05, L06, L07, S07, S09 and Mazda EZ6(6e), primarily covering Navigation Agent and Voice Agent modules.
- Navigation Agent scenario design: optimised 50+ multi-command intents and added recognition for 20+ ambiguous scenario-based command intents across navigation, POI, route planning and travel decisions. Worked with the NLU team to upgrade traditional search-based navigation to a Navigation Agent, increasing user-intent recognition by approximately 135%.
- Voice Agent experience optimisation: analysed 100+ issues involving in-car voice commands, multi-turn dialogue, task interruption and misrecognition. Added on-device voice capabilities to address conventional large-model latency and improve intent recognition; average voice response time across 20+ task intents fell to approximately 20% of its previous level.
- EasyClaw(首个国产龙虾)AI 工作流设计:设计自媒体、产品、运营、客服等行业的 12 个 Agent,为 EasyClaw 产品拓宽适合业务方向
- 设计多 Agent 自动运营系统 - xc.easyclaw.com:覆盖运营、客服、KOL 等 6 条业务线,为增长组提升效率 400%,EasyClaw 官推涨粉至 418%
- 设计龙虾社区 - club.easyclaw.com:设计 UGC + AI Agent 模式,收集 200+ 用户真实使用场景案例和痛点,反馈至产品优化方向完成闭环
- EasyClaw AI workflow design: designed 12 Agents for content creation, product, operations and customer service, expanding applicable business directions for EasyClaw, described in the source resume as the first domestic OpenClaw-style product.
- Multi-Agent automated operations system - xc.easyclaw.com: covered six business lines including operations, customer service and KOLs, increased growth-team efficiency by 400%, and brought EasyClaw's official social following to 418% of its baseline.
- Lobster Community - club.easyclaw.com: designed a UGC + AI Agent model, collected 200+ real user scenarios and pain points, and fed findings back into product improvements to close the feedback loop.
- 构建用户问卷体系:调研用户,明确核心功能:地图导航、兴趣点推荐、活动提示
- 引入游戏化机制:签到奖励、探索成就系统,设计以“任务驱动”和“探索成就”为核心的游戏化界面
- 组织并参与用户测试:收集反馈并进行迭代优化,确保应用程序易用且符合用户预期
- User survey framework: researched users to define map navigation, POI recommendations and event notifications as core features.
- Gamification: designed task-driven and exploration-achievement interfaces with check-in rewards and exploration achievements.
- User testing: organised and participated in testing, collected feedback and iterated to improve usability and meet user expectations.
- 用户与场景分析:分析城市出行需求、驾驶者视野、乘坐姿态与操作触点,提炼用户偏好与座舱功能布局
- 车辆与座舱方案:围绕外观比例、内饰分区、HMI 触点与空间可行性进行,建立车辆设计与交互体验连接
- 高强度迭代:每日 14 小时训练草图、线条、比例与功能完整性表达,持续优化概念方案的完整度
- User and scenario analysis: examined urban mobility needs, driver visibility, seating posture and control touchpoints to identify user preferences and cockpit layouts.
- Vehicle and cockpit concept: connected vehicle design with interaction experience through exterior proportions, interior zoning, HMI touchpoints and spatial feasibility.
- Intensive iteration: practised sketching, linework, proportions and functional expression for 14 hours per day to refine concept completeness.