小鹏汽车Research Scientist (Audio)
任职要求
1、计算机、电子工程、人工智能等相关领域硕士及以上学历; 2、在语音多模态方向具备扎实积累,熟悉多模态任务建模,跨模态模型设计与训练经验; 3、具有扎实的机器学习算法基础,在语音处理、自然语言处理等相关专业领域有研究经验,曾以第一作者身份在ACL/Interspeech/ICASSP/CVPR/CoRL/ICRA/NeurIPS/ICLR/ICML等顶会顶刊上发表过论文; 4、熟练使用PyTorch/TensorFlow等深度学习框架,具备良好的代码实现能力; 5、具有良好的团队合作能力和沟通能力。 加分项 1、计算机、电子工程、信号处理、人工智能、机器人等相关领域博士学历; 2、有语音多模态、大模型、机器人相关研究和项目经验,有国际影响力的论文主要作者或项目主导者; 3、具有优秀的代码能力,如ACM/ICPC、NOI/IOl、Top Coder、Kaggle等比赛获奖者; 4、具备解决复杂问题的经验,并能比较各种解决方案,并根据不同的视角确定前进方向。具备基于机器学习和/或深度学习方法构建系统的经验。
工作职责
1、面向机器人语音交互,打造行业领先的语音大模型,支撑真实世界中的自然、多轮、低时延、人机语音交互,并可在机器人/边缘设备高效部署。形成持续的技术影响力并引领国际行业发展。
1、面向机器人语音交互,打造行业领先的语音大模型,支撑真实世界中的自然、多轮、低时延、人机语音交互,并可在机器人/边缘设备高效部署。形成持续的技术影响力并引领国际行业发展。
• Ship features with PM & Engineering. Co‑own scenario goals; translate product requirements into scientific plans and productionized solutions that meet quality/latency/cost targets. • Model development & optimization. Design, fine‑tune, and evaluate models for LLM‑based authoring, summarization, reasoning, voice/chat, and personalization (e.g., SFT, alignment, prompt/tool use, safety filtering, multilingual & multimodal). • Data & evaluation at scale. Build/extend data pipelines for curation/labeling/feature stores; author offline eval harnesses; run online A/Bs and interleavings; define guardrails and success metrics; author scorecards and decision memos. • Production ML engineering. contribute to service code and configs; add monitoring, tracing, dashboards, and auto‑scaling; participate in on‑call and postmortems to improve live‑site reliability. • Responsible AI. Produce review artifacts, document mitigations for safety/privacy/fairness, support red‑teaming and sensitive‑use checks, and align with Microsoft’s Responsible AI Standard. • Collaboration & mentoring. Partner across PM/ENG/Design/CE/ORA/CELA; share methods and code, review PRs, improve reproducibility and documentation; mentor junior scientists.
• Ship features with PM & Engineering. Co‑own scenario goals; translate product requirements into scientific plans and productionized solutions that meet quality/latency/cost targets. • Model development & optimization. Design, fine‑tune, and evaluate models for LLM‑based authoring, summarization, reasoning, voice/chat, and personalization (e.g., SFT, alignment, prompt/tool use, safety filtering, multilingual & multimodal). • Data & evaluation at scale. Build/extend data pipelines for curation/labeling/feature stores; author offline eval harnesses; run online A/Bs and interleavings; define guardrails and success metrics; author scorecards and decision memos. • Production ML engineering. contribute to service code and configs; add monitoring, tracing, dashboards, and auto‑scaling; participate in on‑call and postmortems to improve live‑site reliability. • Responsible AI. Produce review artifacts, document mitigations for safety/privacy/fairness, support red‑teaming and sensitive‑use checks, and align with Microsoft’s Responsible AI Standard. • Collaboration & mentoring. Partner across PM/ENG/Design/CE/ORA/CELA; share methods and code, review PRs, improve reproducibility and documentation; mentor junior scientists.
• Lead the development and evaluation of state-of-the-art models for code completion and editing, pushing the boundaries of code understanding, generation, fix and review. • Develop retrieval-augmented systems that improve a model’s awareness of large and complex codebases, enabling context-rich code assistance. • Design and prototype efficient inference algorithms to enable fast, interactive code generation experiences at • Collaborate across disciplines with product teams across Microsoft and Github • Stay up to date with the research literature and product advances in AI for software engineering