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 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 Principal Machine Learning Engineer, you will set technical direction for one or more of the hardest problems on the team: extending the B2B data graph into the long tail, resolving entity identity at scale, and reading buying intent from meaning rather than keywords. You'll own outcomes end to end, choosing classical machine learning, statistics, or language models based on what the problem calls for, not habit.
**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 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, setting technical direction and raising the engineering bar through design review and mentorship.
**What You Bring**
_Must-Have:_
- You have taken machine learning systems to production and owned them after launch, with technical leadership as an individual contributor — setting direction for a problem area, leading 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, along with applied statistics: 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, failure analysis from traces, and 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.
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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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ZoomInfo may use a software-based assessment as part of the recruitment process. More information about this tool, including the results of the most recent bias audit, is available [here](https://www.zoominfo.com/legal/nyc-local-law-144-notice).
ZoomInfo is proud to be an equal opportunity employer, hiring based on qualifications, merit, and business needs, and does not discriminate based on protected status. We welcome all applicants and are committed to providing equal employment opportunities regardless of sex, race, age, color, national origin, sexual orientation, gender identity, marital status, disability status, religion, protected military or veteran status, medical condition, or any other characteristic protected by applicable law. We also consider qualified candidates with criminal histories in accordance with legal requirements.
For Massachusetts Applicants: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. ZoomInfo does not administer lie detector tests to applicants in any location.