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, Flamingo) in practical application such as model training, evaluation, and optimization. • - Hands-on experience with LLMs and GenAI frameworks (e.g. LangChain, LlamaIndex) for developing and optimizing AI-driven applications • - Familiarity with embedding, retrieval algorithms, agents, and data modeling for vector development graphs. • - Proven experience managing complex projects and collaborating across cross-functional teams • - Detail-oriented to keep track of and understand the workings of sophisticated algorithms. • - Strong experience articulating and translating business questions into data solutions. • - Curious, self-motivated, and able to drive improvements to model evaluation pipelines and annotation programs. • - Eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive environment. • - Outstanding communication skills – both written and verbal – with experience presenting to leadership.
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、基于最新的大模型、深度学习、机器学习、统计学和优化技术，开发创新算法并为业务问题构建原型； 2、通过无监督学习、聚类算法等技术，从海量数据中发现潜在的模式和趋势，提出数据驱动的业务解决方案； 3、与产品经理和跨职能团队合作，定义用户故事和成功指标，管理数据项目从0到1的全过程； 4、使用AB测试等方法验证项目的商业价值和预期收益，并持续优化模型性能； 5、与工程团队合作部署数据模型，并将解决方案规模化。 1.Develop innovative algorithms and build prototypes for business problems using the latest deep learning, machine learning, statistical, and optimization techniques; 2.Use unsupervised learning and clustering algorithms to discover potential patterns and trends from large datasets and propose data-driven business solutions; 3.Collaborate with product managers and cross-functional teams to define user stories and success metrics, managing data projects from 0 to 1; 4.Use methods like AB testing to validate the business value and expected revenue of projects and continuously optimize model performance; 5.Work with engineering teams to deploy data models and scale solutions.
• Design and implement end-to-end data pipelines (ETL) to ensure efficient data collection, cleansing, transformation, and storage, supporting both real-time and offline analytics needs. • Develop automated data monitoring tools and interactive dashboards to enhance business teams’ insights into core metrics (e.g., user behavior, AI model performance). • Collaborate with cross-functional teams (e.g., Product, Operations, Tech) to align data logic, integrate multi-source data (e.g., user behavior, transaction logs, AI outputs), and build a unified data layer. • Establish data standardization and governance policies to ensure consistency, accuracy, and compliance. • Provide structured data inputs for AI model training and inference (e.g., LLM applications, recommendation systems), optimizing feature engineering workflows. • Explore innovative AI-data integration use cases (e.g., embedding AI-generated insights into BI tools). • Provide technical guidance and best practice on data architecture and BI solution
我们正在寻找AI/ML技术的技术专家，包括计算机视觉(CV)，自然语言处理(NLP)和音频信号处理。您将负责与各种利益相关者(产品、运营、政策和工程)合作，并开发最先进的模型。 1、利用自然语言处理、机器学习或计算机视觉等内容理解能力设计和构建产品核心能力，提取数据洞察并优化变现策略； 2、基于最新的深度学习、机器学习、统计和优化技术的算法开发创新性解决方案并构建业务问题原型； 3、从0到1管理数据项目，并与产品经理协作定义用户故事和成功指标来指导开发过程； 4、使用不限于AB测试等方法验证项目的商业价值和预计收益； 5、与工程团队合作部署数据模型并将解决方案规模化。 We are looking for generalists and specialists in AI/ML techniques, including computer vision (CV), natural language processing (NLP), and audio signal processing. You will be responsible for partnering with a variety of stakeholders (product, operations, policy, and engineering) and developing state-of-the-art models. 1. Design and build core capabilities by leveraging content understanding; capabilities, such as natural language processing, machine learning, or computer vision, to extract insights and improve monetization strategies; 2. Develop creative solutions and build prototypes for business problems using algorithms based on the latest deep learning, machine learning, statistics, and optimization techniques; 3. Independently manage data projects from 0 to 1, and collaborate with product managers to define user stories, and success metrics to guide the development process; 4. Verify the business value and estimated revenue of the project using methods such as AB testing; 5. Collaborate with engineering teams to deploy and scale data science solutions.
NVIDIA’s Solution Architect team is looking for a AI-focused Solution Architect with expertise in Large Language Model, generative AI, or recommender system. We work with the most exciting computing hardware and software, driving the latest breakthroughs in artificial intelligence. We need individuals who can enable customer productivity and develop lasting relationships with our technology partners, making NVIDIA an integral part of end-user solutions. We are looking for someone always thinking about artificial intelligence, someone who can maintain constructive collaboration in a fast paced, rapidly evolving field, someone able to coordinate efforts between corporate marketing, industry business development and engineering. You will be working with the latest AI architecture coupled with the most advanced neural network models, changing the way people interact with technology.As a Solutions Architect, you will be the first line of technical expertise between NVIDIA and our customers. Your duties will vary from working on proof-of-concept demonstrations, to driving relationships with key executives and managers to evangelize accelerated computing. Dynamically engaging with developers, scientific researchers, data scientists, IT managers and senior leaders is a meaningful part of the Solutions Architect role and will give you experience with a range of partners and concerns. What you’ll be doing: • Assisting field business development in guiding the customer build/extend their GPU infrastructures for AI. • Help customers build their large-scale projects, especially Large Language Model (LLM) projects. • Engage with customers to perform in-depth analysis and optimization to ensure the best performance on GPU architecture systems. This includes support in optimization of both training and inference pipelines. • Partner with Engineering, Product and Sales teams to develop, plan best suitable solutions for customers. Enable development and growth of product features through customer feedback and proof-of-concept evaluations. • Build industry expertise and become a contributor in integrating NVIDIA technology into Enterprise Computing architectures.