首席机器学习工程师
Principal Machine Learning Engineer
ZoomInfo 是职业加速的地方。我们行动迅速，思考大胆，并赋予你实现人生最佳工作的能力。你将与 deeply 关心团队、彼此挑战并庆祝胜利的同事一起工作。凭借能放大你影响力的工具和支撑你抱负的文化，你不仅仅会做出贡献。你会快速实现目标。
你将构建 ZoomInfo AI 代理所依据的所有智能信息——关于公司和其中人员的真实情况、它们之间的关系以及它们正在购买的东西。作为首席机器学习工程师，你将为团队中最困难的问题之一设定技术方向：将 B2B 数据图扩展到长尾部分，大规模解决实体身份问题，并从内容含义而非关键词中读取购买意图。你将全程负责结果，根据问题需求选择经典机器学习、统计方法或语言模型，而不是习惯。
**你将做什么**
- 你将把 ZoomInfo 的数据图扩展到那些公开信息很少的公司，从公司网站提取领导层、地点和产品，并检测过时记录。
- 你将预测数据图尚未掌握的信息，使用梯度提升树、缺失输入的回归和校准不确定性来估算未充分记录公司的员工数量和收入。
- 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。
- 你将构建研究公司并引用来源的代理，设计能够区分正确结果与仅完成任务的运行的评估方法。
- 你将把大型模型压缩成在全量数据集上成本效益更高的小型模型，负责量化并作为同一工作的一部分进行部署。
- 你将从模糊、高风险的问题中将其从无定义变为交付，通过设计评审和指导提升工程标准，设定技术方向。
**你将带来什么**
_必备条件：_
- 你有将机器学习系统投入生产并发布后继续负责的经验，作为个体贡献者具备技术领导力——为问题领域设定方向，主导设计评审并指导他人；深度比年限更重要。
- 你拥有经典机器学习经验
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 set the technical direction for the intelligence layer 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 senior individual contributor with no direct reports, you will own strategy across the data graph, entity resolution, and buying intent, staying hands-on in the hardest of these problems. You'll decide when classical machine learning, statistics, or language models is the right tool, and turn good judgment into standards other teams adopt.
**What You'll Do**
- You will set technical strategy for the data graph, entity resolution, buying intent, and agent context, deciding which problems to take on, in what order, and how the pieces connect.
- You will frame and ship problems nobody has framed yet, such as extraction for companies with no public footprint or semantic intent at web scale, deciding what not to build as deliberately as what to build.
- You will define evaluation standards that other teams adopt, including sampling methodology, human labels, validated LLM judges, and release gates, and know where each one breaks.
- You will define the data contracts between the data graph, intent signals, and the agents that consume them, including agent memory, keeping the system cost-effective to run across the full dataset.
- You will turn good choices into defaults across the group: the baseline every model must beat, how uncertainty is reported, and which metrics get retired when they mislead.
- You will lead design reviews, mentor senior engineers, shape hiring, and represent the team's technical judgment to product and leadership.
**What You Bring**
_Must-Have:_
- You have set technical direction as an individual contributor for a problem area spanning more than one team, with the resulting machine learning systems in production and owned after launch — depth of impact matters more than years or past titles.
- You bring breadth across classical machine learning on large, messy tabular data, applied statistics, language processing at scale, and LLM agents or multi-step systems, with recognized depth in at least one.
- You have defined and driven adoption of evaluation standards beyond your own team for systems with no single right answer, including LLM judges validated against human labels and results reported with their limits.
- You have a track record of changing how a group builds or measures — for example, introducing a baseline or review practice that other teams now use.
- You have made cost and capacity decisions for model training and serving, including when to self-host, distill, or call a hosted model, with cost tracked per unit of work.
- You are hands-on 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 entity resolution, knowledge graph, or web-scale information extraction experience on messy, real-world data.
- You have trained and served open-weight models in PyTorch or an equivalent framework, including GPU capacity planning.
- You have built user memory or context systems for agents, or defended LLM systems against adversarial inputs and prompt injection.
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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.
$192,500—$302,500 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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