机器学习工程师 III
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Machine Learning Engineer IIIPeloton · New York, New York
职位信息来自雇主公开的招聘页面。申请前请务必在雇主官网核实详情。
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发现指数 42/100，仅依据与该职位一起存储的证据计算。
- 新的雇主官方职位
分数构成
- 时效性 （随职位发布时间变化）+18
- 雇主官方来源+15
- 稀有职位+1
- 公司来源健康度+8
该职位未包含：已披露薪资、远程职位、提及签证担保、提及搬迁、未出现在监控的职位板上。
这些理由来自雇主自己的职位描述与我们核实过的来源检查结果。除了已存储的信号之外，我们不做任何推测。
职位描述
机器翻译关于该职位
Peloton 的个性化团队正在寻找一名机器学习工程师，负责为我们跨多个平台的高活跃度会员推动个性化和推荐。你的主要工作重点是通过研究和应用 AI 与 ML 技术进行内容推荐和非内容推荐，优化 Peloton 内容的参与度和发现。你将负责我们 ML 产品的端到端生命周期，从数据工程和基础架构，到构建可扩展的微服务和基于 LLM 的解决方案，为我们的用户提供实时服务。你将与 ML 工程师、软件工程师、产品经理和产品分析师密切合作，测试能够推动会员参与度的想法。你将拥有一个独特的机会，处理健身行业中与会员参与度相关的最细粒度数据之一。我们正在寻找对健身充满热情，并对 AI 和机器学习所带来挑战感到兴奋的人，以定义互联健身的未来。
你在 PELOTON 的日常影响
• 构建并改进驱动 Peloton 推荐的 AI 和 ML 流水线
• 研究并应用一流的机器学习技术用于推荐系统
• 评估、实施并改进机器学习模型
• 与我们的产品分析师合作运行 A/B 测试和实验并分析结果
• 设计、部署并监控可扩展的微服务，为高并发机器学习推理端点提供服务
• 开发并扩展评估流水线，以衡量生产环境中的模型性能和偏差
• 设计、实施并维护稳健的微服务，以承载高吞吐量 ML 推理端点
• 架构并管理支持复杂 LLM 功能和实时个性化所需的 ML 基础设施
• 与我们的平台团队密切协作，利用他们的工具和基础设施快速迭代想法，为数百万用户带来愉悦的个性化体验
你为 PELOTON 带来
• 高度定量领域的学位，包括计算机科学、机器学习、运筹学、统计学、数学等
• 3+ 年至少一个以下 ML 领域的工作经验：推荐系统、自然语言处理或计算机视觉
• 对软件工程原则和基础有深入理解，包括数据结构和算法
• 具备使用 Python、Java、Kotlin、Go、C/C++ 编写代码并附带文档以确保可复现性的经验
• 具备关系型和非关系型数据库经验，如 Postgres、MySQL、Cassandra 或 DynamoDB
• 具备向业务、技术和非专业受众撰写和讲解技术概念，并进行数据驱动演示的经验
• 具备设计和部署用于 ML 模型服务的可扩展、低延迟微服务的经验
• 具备现代 MLOps 的实践经验，包括自动化评估流水线和模型监控
• 优先考虑高度定量领域的硕士/博士学位，包括计算机科学、机器学习、运筹学、统计学、数学等
• 优先考虑能够舒适地处理近实时 ML 应用
• 优先考虑有与产品经理合作推出基于 ML 的产品功能的可靠业绩记录
#LI-DD1 #LI-Hybrid 基本薪资范围为
岗位职责
Peloton 的个性化团队正在寻找一名机器学习工程师，负责为我们在多个平台上高度活跃的会员推动个性化和推荐。您的主要工作重点是通过研究和应用 AI 和 ML 技术进行内容和非内容推荐，优化 Peloton 内容的参与度和发现。您将负责我们 ML 产品的端到端生命周期，从数据工程和基础基础设施，到构建可扩展的微服务和基于 LLM 的解决方案，为我们的用户提供实时服务。您将与 ML 工程师、软件工程师、产品经理和产品分析师密切合作，测试能够推动会员参与度的想法。您将拥有一个独特的机会，处理健身行业中与会员参与度相关的最细粒度数据之一。我们正在寻找对健身充满热情，并对 AI 和机器学习的挑战感到兴奋，以定义互联健身未来的人。
您在 PELOTON 的日常工作影响
• 构建并改进驱动 Peloton 推荐的 AI 和 ML 管道
• 研究并应用一流的机器学习技术用于推荐系统
• 评估、实施并改进机器学习模型
• 与我们的产品分析师合作运行 A/B 测试和实验，并分析结果
• 设计、部署并监控可扩展的微服务，为高并发机器学习推理端点提供服务
• 开发并扩展评估管道，以衡量生产环境中模型的性能和偏差
• 设计、实施并维护稳健的微服务，以承载高吞吐量 ML 推理端点
• 架构并管理支持复杂 LLM 功能和实时个性化所需的 ML 基础设施
• 与我们的平台团队密切协作，利用他们的工具和基础设施，快速迭代能够为数百万用户带来愉悦个性化体验的想法
您为 PELOTON 带来的能力
• 计算机科学、机器学习、运筹学、统计学、数学等高度定量领域的学位
• 3 年以上在以下至少一个 ML 领域的工作经验：推荐系统、自然语言处理或计算机视觉
• 对软件工程原则和基础（包括数据结构和算法）有深入理解
• 具有使用 Python、Java、Kotlin、Go、C/C++ 编写代码并附带可复现性文档的经验
• 具有关系型和非关系型数据库经验，如 Postgres、MySQL、Cassandra 或 DynamoDB
• 具有向业务、技术和非专业受众撰写并讲解技术概念，以及进行数据驱动演示的经验
• 具有设计和部署用于 ML 模型服务的可扩展、低延迟微服务的经验
• 具有现代 MLOps 的实操经验，包括自动化评估管道和模型监控
• 优先考虑计算机科学、机器学习、运筹学、统计学、数学等高度定量领域的硕士/博士学位
• 优先考虑能够自如处理近实时 ML 应用
• 优先考虑具有与产品经理合作推出基于 ML 的产品功能的可靠业绩记录
#LI-DD1 #LI-Hybrid 基本薪资范围代表该职位在我们纽约市总部办公时的预期薪资范围下限和上限。该职位实际提供的基本薪资将取决于多种因素，包括但不限于经验和业务目标，以及职位所在地是否发生变化。我们的基本薪资只是 Peloton 具有竞争力的全面薪酬战略的一个组成部分，该战略还包括年度股权奖励和员工股票购买计划，以及其他地区特定的健康与福利福利。
作为一个组织，我们的首要任务之一是维护员工及其家人的健康与福祉。为实现这一目标，我们提供全面而完善的福利，包括：
• 医疗、牙科和视力保险
• 优厚的带薪休假政策
• 短期和长期残疾保险
• 心理健康服务
• 401k、学费报销和学生贷款偿还计划
• 员工股票购买计划
• 生育和收养支持，以及最多 18 周的带薪育儿假
• 儿童照护和家庭照护折扣
• 免费使用 Peloton Digital App，以及服装和产品折扣
• 通勤福利和 Citi Bike 折扣
• 宠物保险等等！
基本薪资范围 $141,400 — $190,700 USD
关于 PELOTON：
Peloton（NASDAQ: PTON）为会员提供专业指导和世界级内容，为任何人、在任何地点、处于健身旅程任何阶段的人创造有影响力和娱乐性的锻炼体验。无论是在家中、户外、旅行途中还是健身房，Peloton 都将创新硬件、独特软件和独家内容结合在一起。Peloton 成立于 2012 年，总部位于纽约市，在美国、英国、加拿大、德国、澳大利亚和奥地利拥有数百万会员。如需了解更多信息，请访问 www.onepeloton.com。
Peloton 是提供平等机会的雇主，并遵守所有适用的联邦、州和地方公平就业实践法律。平等就业机会一直是并将继续是 Peloton 的基本原则。在 Peloton，所有团队成员、申请人和其他受保护人员均根据其个人能力和资格接受考量，不因种族、肤色、宗教、性别、年龄、国籍、残疾、怀孕、遗传信息、军人或退伍军人身份、性取向、性别认同或表达、婚姻和民事伴侣/工会身份、外国人身份或公民身份、信仰、遗传易感性或携带者身份、失业状况、家庭状况、家庭暴力、性暴力或跟踪受害者身份、照护者身份，或适用法律规定的任何其他受保护特征而受到歧视。这项平等就业机会政策适用于与招聘和雇用、薪酬、福利、解雇以及所有其他雇佣条款和条件相关的所有实践和程序。如果您希望从申请到面试过程中请求任何便利安排，请发送电子邮件至：applicantaccommodations@onepeloton.com 。
在 Peloton，我们拥抱技术，包括 AI，以提高生产力并加速我们为会员所做工作的创新。然而，在我们的招聘流程中，我们的首要任务仍然是了解您和您的独特资格。为确保公平公正的流程，我们不允许在申请和面试流程的任何阶段使用 AI 工具。在考虑您作为申请人时，我们希望了解您的技能、经历和动机，而不是通过 AI 系统进行中介。我们也希望在不使用 AI 工具的情况下直接评估您的沟通能力。
有逮捕或定罪记录的合格申请人将根据洛杉矶县雇主公平机会条例和加州公平机会法案、洛杉矶市公平机会招聘倡议条例以及旧金山公平机会条例（如适用于在这些司法管辖区申请职位的申请人）被考虑录用。
请注意，互联网上可能会传播虚假职位空缺、咨询合作、招揽或雇佣机会，以试图获取特权信息，或诱导您为与招聘或培训相关的服务支付费用。Peloton 在招聘或雇用流程的任何阶段都不会收取任何申请、处理或培训费用。所有真实职位空缺都将发布在我们的招聘页面上，Peloton 招聘团队和/或招聘经理的所有沟通都将来自 @ onepeloton.com 电子邮件地址。
如果您对据称来自 Peloton、代表 Peloton 或为 Peloton 发送的电子邮件、信件或电话沟通的真实性有任何疑问，请在就该通信采取任何进一步行动之前发送电子邮件至 applicantaccommodations@onepeloton.com。
Peloton 不接受未经请求的代理机构简历。代理机构不应将简历转发到我们的职位别名、Peloton 员工或任何其他组织地点。Peloton 对与未经请求的简历相关的任何代理机构费用不承担责任。
以上内容由机器翻译自动生成，可能存在错误；投递前请以雇主原文为准。
查看雇主原文
职位描述
ABOUT THE ROLE
The Personalization team at Peloton is looking for a machine learning engineer to drive personalization and recommendations for our highly engaged members across multiple platforms. Your main focus will be to optimize the engagement and discovery of Peloton content through research and application of AI and ML techniques for content and non-content recommendations. You will own the end-to-end lifecycle of our ML products, from data engineering and foundational infrastructure to building scalable microservices and LLM-based solutions that serve our users in real-time. You will work closely with ML Engineers, Software Engineers, Product Managers and Product Analysts to test ideas that drive member engagement. You will have a unique opportunity to work with one of the most granular data related to member engagement in the fitness industry. We’re looking for someone who’s passionate about fitness and is excited about the challenges of AI and machine learning to define the future of connected fitness.
YOUR DAILY IMPACT AT PELOTON
• Build and improve AI and ML pipelines that power Peloton’s recommendations
• Research and apply best-in-class machine learning techniques for recommender systems
• Evaluate, implement, and improve machine learning models
• Run A/B tests and experiments and analyze the results in collaboration with our product analysts
• Engineer, deploy, and monitor scalable microservices that serve high-concurrency machine learning inference endpoints
• Develop and scale evaluation pipelines to measure model performance and bias in production environments
• Design, implement, and maintain robust microservices to host high-throughput ML inference endpoints
• Architect and manage the ML infrastructure necessary to support sophisticated LLM-based features and real-time personalization
• Collaborate and work closely with our platform teams to leverage their tools and infrastructure to rapidly iterate on ideas that drive delightful personalized experiences for millions of users
YOU BRING TO PELOTON
• Degree in highly quantitative fields including Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.
• 3+ years of experience working in at least one of following ML disciplines: recommender systems, natural language processing or computer vision
• Strong understanding of software engineering principles and fundamentals including data structures and algorithms
• Experience writing code in Python, Java, Kotlin, Go, C/C++ with documentation for reproducibility
• Experience with relational and non-relational databases such as Postgres, MySQL, Cassandra, or DynamoDB
• Experience writing and speaking about technical concepts to business, technical, and lay audiences and giving data-driven presentations
• Experience designing and deploying scalable, low-latency microservices for ML model serving
• Hands-on experience with modern MLOps, including automated evaluation pipelines and model monitoring
• MS/PhD in highly quantitative fields including Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc. preferred
• Comfortable working with near real-time ML applications, preferred
• Proven track record of working with product managers to launch ML-based product features, preferred
#LI-DD1 #LI-Hybrid The base salary range r
岗位职责
The Personalization team at Peloton is looking for a machine learning engineer to drive personalization and recommendations for our highly engaged members across multiple platforms. Your main focus will be to optimize the engagement and discovery of Peloton content through research and application of AI and ML techniques for content and non-content recommendations. You will own the end-to-end lifecycle of our ML products, from data engineering and foundational infrastructure to building scalable microservices and LLM-based solutions that serve our users in real-time. You will work closely with ML Engineers, Software Engineers, Product Managers and Product Analysts to test ideas that drive member engagement. You will have a unique opportunity to work with one of the most granular data related to member engagement in the fitness industry. We’re looking for someone who’s passionate about fitness and is excited about the challenges of AI and machine learning to define the future of connected fitness.
YOUR DAILY IMPACT AT PELOTON
• Build and improve AI and ML pipelines that power Peloton’s recommendations
• Research and apply best-in-class machine learning techniques for recommender systems
• Evaluate, implement, and improve machine learning models
• Run A/B tests and experiments and analyze the results in collaboration with our product analysts
• Engineer, deploy, and monitor scalable microservices that serve high-concurrency machine learning inference endpoints
• Develop and scale evaluation pipelines to measure model performance and bias in production environments
• Design, implement, and maintain robust microservices to host high-throughput ML inference endpoints
• Architect and manage the ML infrastructure necessary to support sophisticated LLM-based features and real-time personalization
• Collaborate and work closely with our platform teams to leverage their tools and infrastructure to rapidly iterate on ideas that drive delightful personalized experiences for millions of users
YOU BRING TO PELOTON
• Degree in highly quantitative fields including Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.
• 3+ years of experience working in at least one of following ML disciplines: recommender systems, natural language processing or computer vision
• Strong understanding of software engineering principles and fundamentals including data structures and algorithms
• Experience writing code in Python, Java, Kotlin, Go, C/C++ with documentation for reproducibility
• Experience with relational and non-relational databases such as Postgres, MySQL, Cassandra, or DynamoDB
• Experience writing and speaking about technical concepts to business, technical, and lay audiences and giving data-driven presentations
• Experience designing and deploying scalable, low-latency microservices for ML model serving
• Hands-on experience with modern MLOps, including automated evaluation pipelines and model monitoring
• MS/PhD in highly quantitative fields including Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc. preferred
• Comfortable working with near real-time ML applications, preferred
• Proven track record of working with product managers to launch ML-based product features, preferred
#LI-DD1 #LI-Hybrid The base salary range represents the low and high end of the anticipated salary range for this position based at our New York City headquarters. The actual base salary offered for this position will depend on numerous factors including, without limitation, experience and business objectives and if the location for the job changes. Our base salary is just one component of Peloton’s competitive total rewards strategy that also includes annual equity awards and an Employee Stock Purchase Plan as well as other region-specific health and welfare benefits.
As an organization, one of our top priorities is to maintain the health and wellbeing for our employees and their family. To achieve this goal, we offer robust and comprehensive benefits including:
• Medical, dental and vision insurance
• Generous paid time off policy
• Short-term and long-term disability
• Access to mental health services
• 401k, tuition reimbursement and student loan paydown plans
• Employee Stock Purchase Plan
• Fertility and adoption support and up to 18 weeks of paid parental leave
• Child care and family care discounts
• Free access to Peloton Digital App and apparel and product discounts
• Commuter benefits and Citi Bike Discount
• Pet insurance and so much more!
Base Salary Range $141,400 — $190,700 USD
ABOUT PELOTON:
Peloton (NASDAQ: PTON) provides Members with expert instruction, and world class content to create impactful and entertaining workout experiences for anyone, anywhere and at any stage in their fitness journey. At home, outdoors, traveling, or at the gym, Peloton brings together innovative hardware, distinctive software, and exclusive content. Founded in 2012 and headquartered in New York City, Peloton has millions of Members across the US, UK, Canada, Germany, Australia, and Austria. For more information, visit www.onepeloton.com.
Peloton is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. Equal employment opportunity has been, and will continue to be, a fundamental principle at Peloton, where all team members, applicants, and other covered persons are considered on the basis of their personal capabilities and qualifications without discrimination because of race, color, religion, sex, age, national origin, disability, pregnancy, genetic information, military or veteran status, sexual orientation, gender identity or expression, marital and civil partnership/union status, alienage or citizenship status, creed, genetic predisposition or carrier status, unemployment status, familial status, domestic violence, sexual violence or stalking victim status, caregiver status, or any other protected characteristic as established by applicable law. This policy of equal employment opportunity applies to all practices and procedures relating to recruitment and hiring, compensation, benefits, termination, and all other terms and conditions of employment. If you would like to request any accommodations from application through to interview, please email: applicantaccommodations@onepeloton.com .
At Peloton, we embrace technology, including AI, to enhance productivity and accelerate innovation in the work we do for our members. However, in our hiring process, our priority remains in getting to know you and your unique qualifications. To ensure a fair and equitable process, we do not permit the use of AI tools during any stage of the application and interview process. In considering you as an applicant, we want to understand your skills, experiences, and motivations without mediation through an AI system. We also want to directly assess your communication skills without the use of an AI tool.
Qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act, the City of Los Angeles Fair Chance Initiative for Hiring Ordinance and the San Francisco Fair Chance Ordinance, as applicable to applicants applying for positions in these jurisdictions.
Please be aware that fictitious job openings, consulting engagements, solicitations, or employment offers may be circulated on the Internet in an attempt to obtain privileged information, or to induce you to pay a fee for services related to recruitment or training. Peloton does NOT charge any application, processing, or training fee at any stage of the recruitment or hiring process. All genuine job openings will be posted here on our careers page and all communications from the Peloton recruiting team and/or hiring managers will be from an @ onepeloton.com email address.
If you have any doubts about the authenticity of an email, letter or telephone communication purportedly from, for, or on behalf of Peloton, please email applicantaccommodations@onepeloton.com before taking any further action in relation to the correspondence.
Peloton does not accept unsolicited agency resumes. Agencies should not forward resumes to our jobs alias, Peloton employees or any other organization location. Peloton is not responsible for any agency fees related to unsolicited resumes.