苹果AI Data Scientist
任职要求
Minimum Qualifications • - 3+ years of experience in data science, machine learning, data analysis, or data modeling, with a strong focus on model evaluation, accuracy, and performance metrics. • - Familiarity with vector similarity search, retrieval-augmented generation(RAG) architectures, and LLM prompt evaluation techniques, with experience in integrating these methods into real-world applications • - Advanced programming skills in data manipulation, data processing, and building scalable data pipelines ( SQL & Python preferred). Experience with distributed computing is a plus • - Experience crafting, conducting, analyzing, and interpreting experiments and investigations. • - Comfort with ambiguity, with the ability to structure complex analysis and drive insights through data exploration and strategy research. Preferred Qualifications • - Experience working with multi-modal foundation models (e.g. GPT-4, Gemini 2.5, Claude 3/4, LLaVA, Fl…
工作职责
• In this role, you will • - Evaluate ML & MM-LLM Models: Analyze, validate, and benchmark computer vision, multi-modal, and large language models(LLMs) to ensure they meet accuracy, robustness, and usability standards, utilizing techniques such as A/B testing and cross-validation, and other model evaluation methods • - Develop Metrics: Design and implement performance and evaluation metrics to measure model efficiency, accuracy, and scalability in real-world production environments. • - Failure Analysis: Conduct root cause analysis on model failures across computer vision and multi-modal language model pipelines, identifying improvement areas and collaborating with relevant teams to implement solution. • - Data Processing: Clean, transform, and curate large-scale structured and unstructured datasets, facilitating efficient model evaluation, benchmarking, and testing across diverse data modalities • - Model Optimization: Implement innovative model optimization techniques (e.g. model distillation, quantization, pruning) to improve model scalability, performance, and real-world deployment. • - Collaborate multi-functionally: Collaborate with cross-functional teams, including development, business analysts, and APO teams to integrate models into production. • - Communicate Results: Communicate findings clearly through technical reports, dashboards, and presentations, tailored to both technical and non-technical audiences.
1、参与研发快手公司级的AI数据平台，构建高性能、分布式、可扩展的AI数据引擎，通过数据驱动模型生产，支撑包括 大模型、搜推广 等核心模型的高效迭代； 2、打造业界领先的AI数据引擎，包括高性能实时/离线分布式计算系统、流批一体化的AI数据湖存储系统、CPU/GPU混合计算Ray引擎等，为百万核规模、EB级数据的高效计算、存储、迭代提供易用可靠的基础设施； 3、与算法工程师、研究员团队紧密配合，深刻理解端到端的AI模型研发流程，探索业界前沿的Data4AI技术，负责模型研究中数据工程方案的架构设计、实现、持续迭代和稳定性维护。
我们正在寻找一位 AI 数据产品经理，负责主导 AI 驱动的数据分析产品的规划与落地。你将与算法、工程、数据及业务团队紧密协作，把大模型能力转化为可靠、可用、可持续进化的数据分析产品，让 AI 真正承担起数据分析工作。 岗位职责 - 产品规划与路线图：定义 AI 数据分析产品的能力边界与演进路径，制定并推进产品路线图。 - AI 机会识别与边界管理：判断哪些分析场景适合由 AI 承担、能做到何种程度，将模型能力转化为清晰的产品方案，并持续跟踪模型迭代动态调整边界。 - 需求结构化：深入业务场景，将复杂、模糊的分析需求拆解为 AI 系统可理解、可执行的任务链与知识结构。 - 评估与质量体系：定义评估标准与评测框架，从准确性、稳定性、安全性等维度衡量并驱动模型效果的持续提升。 - 数据资产与飞轮：设计数据回流与反馈机制，推动分析经验、指标口径与业务知识的资产化沉淀，形成"越用越准"的数据飞轮。 - 跨团队协作：作为业务与技术之间的桥梁，推动产品从设计、开发到上线、运营的全生命周期落地。
AI Agent Engineering • Design, develop, and deploy production-grade AI agent systems, including multi-agent orchestration, tool-use frameworks, memory management, and API integration — ensuring reliability, scalability, and maintainability • Build and optimize Retrieval-Augmented Generation (RAG) pipelines: document ingestion, chunking strategy, embedding, vector search, and re-ranking to maximize LLM grounding quality • Support LLM adaptation to WWGS business domains through prompt engineering, context injection, fine-tuning signal curation, and systematic prompt evaluation frameworks • Develop automated knowledge base construction and real-time data access capabilities (Data Agent, MCP server/client) to connect AI agents with live business data • Design and implement LLM evaluation pipelines to systematically assess agent output quality, hallucination risk, and business impact Data Engineering • Design and implement end-to-end data pipelines (batch and streaming) for data collection, transformation, and storage — supporting both AI application and analytics use cases • Build and maintain integration layer data models that serve as a unified, AI-ready data foundation across WWGS domains • Develop automated data quality monitoring, alerting, and observability tooling to ensure pipeline reliability and data trustworthiness • Integrate multi-source data (seller behavior, transaction logs, off-platform signals, AI outputs) into a coherent, governed data layer • Establish data standardization and governance policies ensuring consistency, accuracy, and compliance across AI and BI consumption layers Technical Leadership • Provide technical guidance on AI-data architecture decisions; define best practices for the team's AI agent and data engineering stack • Collaborate cross-functionally with Product, Operations, and Science teams to translate business requirements into scalable technical solutions • Mentor junior engineers and conduct design reviews; raise the technical bar across the team