高级机器学习工程师 II，搜索与推荐排序
查看雇主原标题
Senior Machine Learning Engineer II, Search & Recommendations RankingInstacart · US - Remote
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。
为什么值得关注？
发现指数 50/100，仅依据与该职位一起存储的证据计算。
- 新的雇主官方职位
- 远程职位
分数构成
- 时效性 （随职位发布时间变化）+18
- 雇主官方来源+15
- 远程职位+8
- 稀有职位+1
- 公司来源健康度+8
该职位未包含：已披露薪资、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
机器翻译我们正在改变食品杂货行业
在 Instacart，我们邀请全世界通过食物分享爱，因为我们相信每个人都应该能够获得自己喜爱的食物，并有更多时间一起享用。在别人看到简单的食品杂货配送需求的地方，我们看到了令人兴奋的复杂性和无限机会，来服务我们社区多样化的需求。我们致力于提供一项客户赖以获取食品杂货和家居用品的必要服务，同时也为 Instacart Personal Shoppers 提供安全且灵活的赚钱机会。
Instacart 已成为数百万人的生命线，我们正在组建团队，帮助推动我们的购物车向前。如果你准备好做你一生中最出色的工作，欢迎加入我们的餐桌。
Instacart 是一个 Flex First 团队
对于如何以最佳状态工作，没有一刀切的方法。我们的员工可以灵活选择在哪里以最佳状态工作——无论是在家、办公室，还是你最喜欢的咖啡店——同时通过定期的线下活动保持联系并建设社区。进一步了解我们在工作地点方面的灵活方式。
概述
Search & Personalization ML 团队是 Instacart 实现最先进多任务、多目标排序的引擎——将搜索、发现、推荐、广告和商品运营统一到一个价值感知平台中。我们与世界级工程师、科学家和 PM 合作，构建支撑整个购物旅程每一个像素的排序骨干，不仅优化点击，还优化长期增量 GTV、购物篮提升和留存。
我们正在构建什么
• 基础排序骨干模型：多任务/多目标模型（共享编码器 + 任务头），联合学习相关性、转化、利润贡献、流失风险和广告质量，从而在搜索和推荐中实现一致的决策。
• 价值感知优化：提升和长期价值模型，将决策引导至增量和 LTV，并对质量、多样性、公平性和支出节奏设置校准约束——同时为安全探索设置护栏。
• LLM 增强的检索与特征：使用 LLM 丰富查询和商品语义，以支持长尾召回，为冷启动生成特征，并为排序器提供富含推理的上下文，同时仍然是最终排序的事实来源。
我们对 AI 创新的承诺体现在我们近期的出版物和对该领域的研究贡献中。
关于该职位
• 架构排序骨干，将查询理解、个性化、多目标排序、广告和商品运营统一到一个自适应平台中。
• 设计并构建针对个性化和基于价值的相关性优化的搜索自动建议系统。
• 设计长期目标函数（例如增量、LTV、习惯形成），并构建超越短期参与度的提升/因果价值模型。
• 开发生产级多任务学习（例如共享编码器、MMOE/PLE 任务头），联合学习相关性、倾向、利润和流失风险——确保校准、约束和可解释性。
• 负责推理层：目标感知重排器、多样性和质量约束、安全探索，以及毫秒级延迟优化。
• 推进评估实践：在线实验、长期队列指标、反事实评估，以及用于跟踪增量 GTV 和留存的归因管道。
• 与广告、基础设施、产品和设计团队合作，将业务目标转化为排序策略和可衡量的 ROI。
• 指导 ML 工程师，建立排序、因果推断和可扩展服务系统方面的专业能力。
任职要求
• 5+ 年大规模应用 ML 的经验（3+ 年技术领导经验），并有在生产环境中改进排序或推荐系统的可靠业绩记录。
• 在应用多目标或约束优化以平衡相关性、收入、利润和用户体验方面有成功经验；有在线测试和超越 CTR 的归因经验。
• 较强的编码能力（Python）和数据能力（SQL/Pandas），并精通经典 ML 技术（例如 XGBoost）和深度学习框架（TensorFlow/PyTorch）。
• 出色的分析能力和较强的跨职能沟通能力。\
• 机器学习、统计学、计算机科学、信息检索或密切相关领域的学位（硕士或博士）。
• 精通多任务学习架构（例如 MMOE/PLE、共享编码器）、校准、反事实评估、提升/因果建模，和/或用于探索的上下文老虎机。
• 有构建低延迟排序服务的经验，包括特征存储、缓存、向量 + 词法检索、重排和 A/B 测试基础设施，并精通约束感知推理。
• 有将 LLM 作为特征/召回增强器的实操经验（例如嵌入、适配器调优），同时清楚何时应由排序器进行裁决。
Instacart 在我们员工工作的每个地点都提供极具市场竞争力的薪酬和福利。该职位为远程职位，成功候选人的基本薪资范围取决于其长期工作地点。请在此处查看我们的 Flex First 远程工作政策。
录用条件可能因许多因素而异，例如候选人的经验和该职位所需的技能。此外，该职位还可获得新员工股权授予以及年度刷新授予。请在此处进一步了解我们的福利。
对于美国候选人，成功候选人的基本薪资范围如下。
CA, NY, CT, NJ $207,000 — $253,500 USD
WA $198,000 — $243,000 USD
OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI $190,000 — $233,000 USD
所有其他州 $173,000 — $212,000 USD
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
We're transforming the grocery industry
At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers.
Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.
Instacart is a Flex First team
There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work.
Overview
The Search & Personalization ML team is Instacart’s engine for state-of-the-art multi-task, multi-objective ranking—unifying search, discovery, recommendation, ads, and merchandising into a single value-aware platform. Partnering with world-class engineers, scientists, and PMs, we build the ranking backbone that powers every pixel of the shopping journey, optimizing not just for clicks, but for incremental GTV, basket lift, and retention over the long run.
What We’re Building
• Foundational Ranking Backbone Models: Multi-task/multi-objective models (shared encoders + task heads) that jointly learn relevance, conversion, margin contribution, churn risk, and ad quality, enabling consistent decisions across search and recommendations.
• Value-Aware Optimization: Uplift and long-horizon value models that steer decisions toward incrementality and LTV, with calibrated constraints on quality, diversity, fairness, and spend pacing—plus guardrails for safe exploration.
• LLM-Enhanced Retrieval & Features: Using LLMs to enrich query and item semantics for long-tail recall, generate features for cold-starts, and feed the ranker with reasoning-rich context, while remaining the source of truth for final ordering.
Our commitment to AI innovation is reflected in our recent publications and research contributions to the field.
About the Job
• Architect the ranking backbone that unifies query understanding, personalization, multi-objective ranking, ads, and merchandising into a single adaptive platform.
• Design and build a search autosuggest system optimized for personalization and value-based relevance.
• Design long-horizon objective functions (e.g., incrementality, LTV, habit formation) and build uplift/causal value models that move beyond short-term engagement.
• Develop production-grade Multi-Task Learning (e.g., shared encoders, MMOE/PLE task heads) to jointly learn relevance, propensity, margin, and churn risk—ensuring calibration, constraints, and explainability.
• Own the inference layer: goal-aware re-rankers, diversity and quality constraints, safe exploration, and millisecond-class latency optimization.
• Advance evaluation practices: online experiments, long-horizon cohort metrics, counterfactual evaluations, and attribution pipelines for tracking incremental GTV and retention.
• Partner across ads, infrastructure, product, and design teams to translate business goals into ranking policies and measurable ROI.
• Mentor ML engineers to build expertise in ranking, causal inference, and scalable serving systems.
任职要求
• 5+ years applying ML at scale (3+ years in technical leadership), with a proven track record improving ranking or recommendation systems in production.
• Demonstrated success in applying multi-objective or constrained optimization to balance relevance, revenue, margin, and user experience; experience with online testing and attribution beyond CTR.
• Strong coding (Python) and data fluency (SQL/Pandas), with expertise in classic ML techniques (e.g., XGBoost) and deep learning frameworks (TensorFlow/PyTorch).
• Excellent analytical skills and strong cross-functional communication abilities.\
• Graduate degree (Masters or PhD) in machine learning, statistics, computer science, information retrieval, or a closely related field.
• Expertise in multi-task learning architectures (e.g., MMOE/PLE, shared encoders), calibration, counterfactual evaluation, uplift/causal modeling, and/or contextual bandits for exploration.
• Experience building low-latency ranking services, including feature stores, caching, vector + lexical retrieval, re-ranking, and A/B testing infrastructure, with expertise in constraint-aware inference.
• Hands-on experience with LLMs as feature/recall enhancers (e.g., embeddings, adapter tuning) while maintaining clarity on when the ranker should arbitrate.
Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here .
Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please rea d more about our benefits offerings here .
For US based candidates, the base pay ranges for a successful candidate are listed below.
CA, NY, CT, NJ $207,000 — $253,500 USD
WA $198,000 — $243,000 USD
OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI $190,000 — $233,000 USD
All other states $173,000 — $212,000 USD