远程工作雷达

资深+软件工程师,隐私

Staff+ Software Engineer, Privacy

开发工程全球可投
公司Anthropic
薪资未公开
工作地点Remote-Friendly (Travel-Required) | San Francisco, CA | Seattle, WA | New York City, NY
地域资格全球可投
时区要求无特别要求
用工类型未标注
发布时间2026-08-07
数据来源Greenhouse
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全球可投:该职位未限制候选人所在地区。仍需注意薪资可能按地区折算,以及实际签约方式(正式雇佣 / 独立合同)。

关于 Anthropic

Anthropic 的使命是创造可靠、可解释且可引导的 AI 系统。我们希望 AI 对我们的用户以及整个社会都是安全且有益的。我们的团队是一个快速发展的由致力于研究、工程、政策专家和商业领袖组成的群体,共同构建有益的 AI 系统。

关于该职位

Anthropic 正在开发处理大量敏感信息的前沿 AI 系统。我们如何保护这些数据,以及如何将隐私性融入系统设计而非后期添加,是我们构建安全且有益 AI 的核心。

这是一个基础性角色。作为我们首批专门的隐私工程师之一,你将帮助建立 Anthropic 的隐私工程职能,并从一开始就塑造隐私如何被设计进我们的 AI 系统。你将加入我们的数据基础设施团队,设计隐私保护系统,领导隐私增强技术在我们基础设施中的实施,并在工程、研究和产品团队中提供隐私方面的技术领导力。

你将在隐私工程、AI 安全和分布式系统之间工作,解决尚未有明确答案的问题。这是一个具有高度自主权和广泛影响力的高级个人贡献者职位。

主要职责

  • 为大规模运行的 AI 训练和推理系统设计并实现隐私保护架构,使用如差分隐私、联邦学习和安全多方计算等技术
  • 与研究人员合作,实现保护用户数据的隐私保护训练方法,同时保持模型质量
  • 构建基础隐私基础设施,包括自动化数据发现、分类、访问控制、审计日志和生命周期管理
  • 将监管要求(如 GDPR、CCPA、HIPAA、欧盟 AI 法案)转化为技术实现和自动化合规控制
  • 设计数据治理系统,用于追踪分布式 AI 系统中的数据来源、用途限制和保留策略
  • 主导新模型和功能的隐私审查和威胁建模,识别风险并设计可扩展的缓解措施
  • 与产品和基础设施团队合作,将隐私控制嵌入 Claude 的推理系统、用户界面和数据管道
  • 开发隐私工程工具包和框架
查看英文原文

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role

Anthropic is working on frontier AI systems that handle sensitive information at enormous scale. How we protect that data, and how we build privacy into our systems rather than bolting it on afterward, is central to our mission of building AI that is safe and beneficial.

This is a foundational role. As one of our first dedicated privacy engineers, you will help establish the privacy engineering function at Anthropic and shape how privacy is designed into our AI systems from the ground up. You'll sit within our Data Infrastructure team, architecting privacy-preserving systems, leading the implementation of privacy-enhancing technologies across our infrastructure, and providing technical leadership on privacy across engineering, research, and product teams.

You'll work at the intersection of privacy engineering, AI safety, and distributed systems, solving problems that don't yet have established answers. This is a senior individual contributor role with high autonomy and broad influence.

Key responsibilities

  • Design and implement privacy-preserving architectures for AI training and inference systems operating at very large scale, using techniques e.g. differential privacy, federated learning, and secure multi-party computation
  • Partner with researchers to implement privacy-preserving training methods that protect user data while maintaining model quality
  • Build foundational privacy infrastructure, including automated data discovery, classification, access controls, audit logging, and lifecycle management
  • Translate regulatory requirements (e.g., GDPR, CCPA, HIPAA, the EU AI Act) into technical implementations and automated compliance controls
  • Architect data governance systems for tracking data lineage, purpose limitation, and retention across distributed AI systems
  • Lead privacy reviews and threat modeling for new models and features, identifying risks and designing scalable mitigations
  • Partner with product and infrastructure teams to embed privacy controls into Claude's inference systems, user interfaces, and data pipelines
  • Develop privacy engineering toolkits and frameworks that enable other engineers to build privacy-preserving features by default
  • Design privacy-preserving analytics and measurement systems that surface useful insights without exposing individual user data
  • Evaluate emerging privacy technologies from academia and industry, and contribute to open-source tooling and AI privacy standards
  • Advise on and advocate for privacy practices as a core part of how we approach AI safety

Minimum qualifications

  • Experience applying privacy engineering principles in production systems, including privacy by design, data minimization, and purpose limitation
  • Proficiency in Python, Go, or similar languages, with experience building and operating production systems at scale
  • Experience designing and implementing privacy infrastructure for systems with a large user base
  • Experience with data governance, classification, or data lifecycle management systems
  • Understanding of privacy regulations such as GDPR and CCPA, and the ability to translate legal requirements into technical designs
  • Experience conducting privacy reviews, threat modeling, or risk assessments
  • Written and verbal communication skills sufficient to build alignment across engineering, research, legal, and product teams

Preferred qualifications

  • Hands-on experience with privacy-enhancing technologies (e.g., differential privacy, homomorphic encryption, secure enclaves, secure multi-party computation)
  • Experience building privacy infrastructure or controls for machine learning or AI systems
  • Experience establishing a privacy engineering practice, or being an early hire in a function
  • Experience with distributed systems and cloud infrastructure at scale
  • Experience serving as a technical lead on complex, multi-quarter projects
  • Contributions to open-source privacy tooling, privacy research, or industry standards
  • 12+ years of experience in a software engineering role, including building and operating large-scale infrastructure
  • 3+ years of experience leading large, complex projects as a technical lead

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:
$405,000—$485,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

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