机器学习工程师
Machine Learning Engineer
ZoomInfo 是职业加速的地方。我们行动迅速，思考大胆，并赋能你完成一生中最好的工作。你会与 deeply 关心工作的队友一起工作，他们互相挑战并庆祝成功。借助能放大你影响力的工具和一个支持你抱负的文化，你不仅仅会做出贡献。你会快速实现目标。
你将构建 ZoomInfo AI 代理所依赖的智能——关于公司及其人员的真实信息、它们之间的关系以及它们正在购买的内容。作为机器学习工程师，你将负责团队在扩展 ZoomInfo 的 B2B 数据图谱至公开信息极少的长尾公司方面的最终结果，解决大规模实体身份识别问题，并从内容含义而非关键词中读取购买意图。你将根据问题需求应用经典机器学习、统计学或现代语言模型，从决策到生产有最短路径。
**你将做什么**
- 你将扩展 ZoomInfo 的数据图谱，覆盖那些公开信息极少的长尾公司，从公司网站提取领导层、地点和产品信息，并检测过时记录。
- 你将预测图谱尚未掌握的信息，使用梯度提升树、缺失输入的回归和校准不确定性，对文档不全的公司进行员工数和收入估计。
- 你将判断两条记录是否描述同一家公司或同一个人，衡量错误合并和错误拆分的结果。
- 你将从大量多语言、噪声文本的网页内容中推断购买意图，为倾向评分、类似客户检索和联系人推荐提供支持。
- 你将构建能够研究公司并引用其来源的代理，并设计评估体系以区分正确结果与仅完成的运行。
- 你将把大型模型提炼为可在整个数据集上高效运行的小型模型，负责量化并作为同一工作的一部分。
- 你将从模糊、高风险的问题中将其从无定义状态变为交付，通过评审、设计和指导提升团队的工程标准。
_**你带来的能力**_
**必备条件：**
- 你曾将机器学习系统投入生产并在此后负责其运行，跟踪结果进入所需的任何层级，作为个体贡献者具备技术领导力：为问题领域设定方向、进行设计评审和指导。深度比年限更重要
查看英文原文
ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute. You’ll make things happen–fast.
You'll build the intelligence every ZoomInfo AI agent reasons over — what's true about companies and the people in them, how they relate, and what they're buying. As a Machine Learning Engineer, you will own outcomes end-to-end on the team extending ZoomInfo's B2B data graph into the long tail of companies with little public information, resolving entity identity at scale, and reading buying intent from meaning rather than keywords. You will apply classical machine learning, statistics, or modern language models, whichever the problem calls for, with a short path from decision to production.
**What You'll Do**
- You will extend ZoomInfo's data graph into the long tail of companies with little public footprint, extracting leadership, locations, and products from company websites and detecting stale records.
- You will predict what the graph doesn't know yet, estimating headcount and revenue for under-documented companies using gradient-boosted trees, regression with missing inputs, and calibrated uncertainty.
- You will determine whether two records describe the same company or person, measuring both wrongly merged and wrongly split outcomes.
- You will infer buying intent from the meaning of web content across large volumes of multilingual, noisy text, feeding propensity scoring, lookalike retrieval, and contact recommendations.
- You will build agents that research companies and cite their sources, and design the evaluations that separate a correct result from a run that merely finished.
- You will distill large models into smaller ones that run cost-effectively across the full dataset, owning quantization and serving as part of the same work.
- You will take ambiguous, high-stakes problems from undefined to shipped, and raise the team's engineering bar through review, design, and mentorship.
_**What You Bring**_
**Must-Have:**
- You have taken machine learning systems to production and owned them after launch, following the outcome into whichever layer it needs, with technical leadership as an individual contributor: setting direction for a problem area, design review, and mentoring. Depth matters more than years.
- You bring classical machine learning expertise beyond language models, including supervised learning and feature engineering on large, messy tabular data.
- You apply statistics to decisions: experiment design, statistical inference, and calibrated scores under class imbalance.
- You have deployed language processing at scale — text classification, information extraction, and entity linking over large volumes of multilingual, noisy text.
- You have built and operated LLM agents or multi-step systems in production, including tool and context design and failure analysis from traces, with evaluation for systems with no single right answer using LLM judges validated against human labels.
- You are proficient in production Python and strong SQL with distributed data processing experience, and you use AI coding tools daily with rigorous review of their output.
**Preferred:**
- You have experience with ranking and retrieval, including embeddings, learned re-ranking, and metrics such as recall@k, MRR, and nDCG.
- You bring propensity modeling, clustering, or entity resolution experience on messy, real-world data.
- You have trained and served open-weight models in PyTorch or an equivalent framework, tracking cost per unit of work.
- You have defended LLM systems against adversarial inputs and prompt injection, or worked on web-scale information extraction, knowledge graphs, or user memory for agents.
#LI-Remote
#LI-VC1
Actual compensation offered will be based on factors such as the candidate’s work location, qualifications, skills, experience and/or training. Your recruiter can share more information about the specific salary range for your desired work location during the hiring process. We want our employees and their families to thrive.
In addition to comprehensive benefits we offer holistic mind, body and lifestyle programs designed for overall well-being. Learn more about ZoomInfo benefits [here](https://www.zoominfo.com/careers#benefits).
Below is the US base salary for this position. Additional compensation such as Bonus, Commission, Equity and other benefits may also apply.
$128,100—$201,300 USD
**About us:**
ZoomInfo (NASDAQ: GTM) is the Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights, trusted data, and advanced automation. Its solutions provide more than 35,000 companies worldwide with a complete view of their customers, making every seller their best seller.
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