高级AI工程师
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Senior AI EngineerGitLab · Remote, US · base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary ra
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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
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职位描述
机器翻译GitLab 是面向 DevSecOps 的智能编排平台。GitLab 帮助组织提升开发者生产力、改善运营效率、降低安全与合规风险,并加速数字化转型。超过 5000 万注册用户以及超过 50% 的《财富》100 强*企业信任 GitLab,以更快交付更优质、更安全的软件。
我们产品中内置的同样原则也体现在我们团队的工作方式中:我们将 AI 视为核心生产力倍增器,所有团队成员都应把 AI 融入日常工作流程,以推动效率、创新和影响力。GitLab 是职业加速发展、创新蓬勃生长、每一个声音都被重视的地方。我们的高绩效文化由我们的价值观和持续的知识交流驱动,使团队成员能够充分发挥潜力,同时与行业领导者协作解决复杂问题。与我们共同创造未来,打造改变世界软件开发方式的技术。
* Fortune 500® 是 Fortune Media IP Limited 的注册商标,经许可使用。该声明基于 GitLab 数据。Fortune 100 指 2025 年《财富》500 强榜单中排名前 20% 的公司,该榜单于 2025 年 6 月发布。Fortune 和 Fortune Media IP Limited 与 GitLab 无关联,也不为 GitLab 的产品或服务背书。
职位概览
作为 GitLab 的高级 AI 工程师,你将帮助构建 GitLab 转型为 AI 优先公司的基础。你将向 Enterprise AI 总监汇报,并作为亲力亲为的技术领导者,负责交付内部 AI 驱动的解决方案,以带来可衡量的业务成果。
快速构建很重要,但仅靠快速构建还不够。这个职位首先要理解真正的问题:梳理工作如何在团队、工具和交接之间流动,识别真正的约束,并在开始开发之前验证 AI 是否是合适的解决方案。在此基础上,你将负责从发现到部署的全过程,将扎实的工程能力与系统思维和业务理解相结合。
你最初的关注范围将涵盖销售、市场营销和客户支持,在这些领域,你将把 AI 解决方案嵌入关键系统和工作流程。这个职位提供了塑造 GitLab 团队成员工作方式、改善组织内流动,并在远程、异步、以价值观驱动的环境中帮助推进我们使命的机会。
岗位职责
• 在构建解决方案之前先诊断业务问题。梳理工作流程,识别约束,并确认 AI 是否是合适的干预手段。当诚实答案是“这不需要 AI”时,也要准备好这样说。
• 端到端负责 AI 计划,从利益相关者发现和技术设计,到实施、部署和迭代。
• 快速设计、开发和交付 AI 驱动的解决方案,在几天而不是几个月内交付可工作的原型,并专注于实际成果和可衡量的业务价值。
• 通过构建减少瓶颈、缩短交付周期并提高吞吐量的解决方案,改善组织流动。使用流动指标以及采用率和 ROI 来衡量成功。
• 使用 API、编排工具和现代 AI 平台(在适当情况下包括 GitLab Duo Agent Platform),将 AI 能力集成到现有系统和工作流程中。合适的工具才是赢家,无论是自定义代码、平台,还是精心设计的提示词。
• 成为 Customer Zero:尽可能利用并展示 GitLab 的 AI 产品,并将真实使用洞察反馈给研发团队。
• 与跨职能利益相关者紧密合作,理解真正的约束。提出正确的问题,连接技术与非技术视角,并在跳到解决方案之前就成果达成一致。
• 通过业务指标、流动指标和反馈循环来定义并跟踪成功,使绩效可见且可执行。
• 通过评估工具、记录模式并创建可复用的基础,帮助团队扩大影响力,从而为技术方向做出贡献。
你将带来什么
• 内心是技术专家 - 真正投入于技术,无论基础技术还是前沿技术都同样重视。你既会为设计良好的 API 集成感到兴奋,也会为最新基础模型发布感到兴奋。你会选择能很好解决问题的最简单方案,而不是在成熟方法足够时强行使用新技术。AI 是你工具包中强大的一部分,但它建立在扎实的工程基础之上,而不是取代这些基础。
• 胜任且自信的编码能力 - 你能够端到端构建可工作的解决方案,编写干净且可维护的代码,并有效调试。无论你的技能是在传统工程岗位中磨练出来的,还是通过构建自动化或交付副业项目获得的,重要的是你能够独立交付生产级工作成果。
• AI 与 LLM 技术深度 - 精通至少一种现代脚本语言(Python、JavaScript/TypeScript 或类似语言),并对 REST API、GraphQL 和集成模式有扎实理解。具备现代 AI 技术的深入实践经验,具体包括:提示词工程作为核心学科:设计有效的系统提示词、管理上下文窗口、构建多轮交互、评估输出质量,并系统性地迭代提示词设计。
• 模型选择与成本性能权衡:理解何时较小的微调模型会优于通用大型模型,何时 RAG 是正确架构而不是扩展上下文窗口,以及如何就能力与成本做出有原则的决策。
• 智能体架构模式:工具使用、多智能体编排、人在回路设计、护栏、评估框架和生产级可靠性模式。对 LLM 生态具备实际熟练度:拥有使用 Anthropic、OpenAI、开源替代方案模型的实际经验,并具备判断何时选择哪一种的能力。
• AI 安全与风险意识 - 你会批判性地思考你所构建的解决方案可能如何被利用、滥用或产生意外后果。你知道如何设计适当的护栏(输入验证、输出过滤、访问控制、提示词注入防御和数据泄露防护),并将这些视为一等工程问题。
• 系统思维与诊断严谨性 - 能够观察复杂流程并看到约束。能够自如地梳理工作如何端到端流动,识别瓶颈,并在提出解决方案之前追溯问题根因。你会本能地先问“这里真正阻碍流动的是什么?”,然后再问“我应该使用哪个模型?”
• 业务系统专长 - 熟悉企业业务系统版图,包括 CRM(Salesforce)、营销自动化(Marketo)、支持平台(Zendesk)、集成与编排工具(Workato)、AI 平台(Relevance AI),以及企业搜索和知识工具(Glean)。你不需要对所有这些都有深入经验,但需要理解它们的作用、如何协同,并愿意围绕它们并跨它们进行构建。对企业数据模型和工作流程有深入理解至关重要。
• 广泛职能理解 - 能够与不同领域的利益相关者进行有意义的对话,并快速理解他们的独特需求。
• 端到端负责 - 有从发现到交付负责复杂计划的经验。能够在模糊环境中自如运作,并独立推动可衡量的成果。
• 产品思维 - 能够界定 MVP 范围、严格排序优先级,并迭代交付。此外,还要考虑采用率、用户体验和业务成果。
优先要求
任职要求
• 具备咨询、解决方案工程或面向客户的技术岗位背景
• 熟悉价值流映射、流动指标或约束理论思维
• 具备低代码/无代码编排工具(n8n、Make、Workato)以及自定义开发经验
• 有初创公司或高增长公司经验
福利待遇
• 灵活带薪休假
• 团队成员资源小组
• 股权薪酬与员工股票购买计划
• 成长与发展基金
• 育儿假
请注意,我们欢迎具有不同经验水平的候选人表达兴趣;许多成功候选人不满足每一项要求。此外,研究表明,来自代表性不足群体的人除非满足每一项资格要求,否则不太可能申请职位。如果你对这个职位感到兴奋,请申请,并让我们的招聘人员评估你的申请。
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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
GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster.
The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software.
* Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab.
An overview of this role
As an Senior AI Engineer at GitLab, you'll help build the foundation for GitLab's transformation into an AI-first company. Reporting to the Director, Enterprise AI, you'll be a hands-on technical leader responsible for delivering internal AI-powered solutions that drive measurable business outcomes.
Building fast matters, but it's not enough on its own. This role starts with understanding the real problem: mapping how work moves across teams, tools, and handoffs, identifying the true constraint, and validating whether AI is the right solution before you begin development. From there, you'll take ownership from discovery through deployment, combining strong engineering skills with systems thinking and business understanding.
Your initial focus will span Sales, Marketing, and Customer Support, where you will embed AI solutions into key systems and workflows. This role offers the opportunity to shape how GitLab team members work, improve flow across the organization, and help advance our mission in a remote, asynchronous, and values-driven environment.
岗位职责
• Diagnose business problems before building solutions. Map workflows, identify constraints, and confirm whether AI is the right intervention. Be prepared to say "this doesn't need AI" when that's the honest answer.
• Own AI initiatives end-to-end, from stakeholder discovery and technical design through implementation, deployment, and iteration.
• Design, develop, and ship AI-powered solutions quickly, delivering working prototypes in days, not months, with a focus on practical outcomes and measurable business value.
• Improve organizational flow by building solutions that reduce bottlenecks, shorten lead times, and increase throughput. Measure success using flow metrics alongside adoption and ROI.
• Integrate AI capabilities into existing systems and workflows using APIs, orchestration tools, and modern AI platforms, including GitLab Duo Agent Platform, where appropriate. The right tool wins, whether that's custom code, a platform, or a well-crafted prompt.
• Be Customer Zero: leverage and showcase GitLab's AI offerings wherever possible, feeding real-world usage insights back to R&D.
• Partner closely with stakeholders across functions to understand the real constraints. Ask the right questions, bridge technical and non-technical perspectives, and align on outcomes before jumping to solutions.
• Define and track success through business metrics, flow metrics, and feedback loops that make performance visible and actionable.
• Contribute to technical direction by evaluating tools, documenting patterns, and creating reusable foundations that help the team scale its impact.
What you'll bring
• A Technologist at Heart - Genuinely invested in technology, the foundational and the cutting-edge in equal measure. You're as energised by a well-designed API integration as you are by the latest foundation model release. You reach for the simplest solution that solves the problem well, rather than forcing new technology when proven approaches would do. AI is a powerful part of your toolkit, but it sits on top of solid engineering fundamentals, not in place of them.
• Competent, Confident Coding Skills - You can build working solutions end-to-end, write clean and maintainable code, and debug effectively. Whether your skills were honed in a traditional engineering role, through building automations, or shipping side projects, what matters is that you can deliver production-quality work independently.
• AI & LLM Technical Depth - Strong proficiency in at least one modern scripting language (Python, JavaScript/TypeScript, or similar) and a solid understanding of REST APIs, GraphQL, and integration patterns. Deep, practical experience with modern AI technologies, specifically: Prompt engineering as a core discipline: designing effective system prompts, managing context windows, structuring multi-turn interactions, evaluating output quality, and iterating systematically on prompt design.
• Model selection and cost-performance trade-offs: understanding when a smaller fine-tuned model outperforms a general-purpose large one, when RAG is the right architecture versus expanding the context window, and how to make principled decisions about capability versus cost.
• Agentic architecture patterns: tool use, multi-agent orchestration, human-in-the-loop designs, guardrails, evaluation frameworks, and production-grade reliability patterns.Practical fluency across the LLM ecosystem: hands-on experience with models from Anthropic, OpenAI, open-source alternatives, and the judgment to know which to reach for and when.
• AI Safety & Risk Awareness - You think critically about how the solutions you build could be exploited, misused, or produce unintended consequences. You know how to design appropriate guardrails (input validation, output filtering, access controls, prompt injection defences, and data leakage prevention) and you treat these as first-class engineering concerns.
• Systems Thinking & Diagnostic Rigour - The ability to look at a complex process and see the constraint. Comfortable mapping how work flows end-to-end, identifying bottlenecks, and tracing problems to root causes before proposing solutions. You instinctively ask "what's actually blocking flow here?" before asking "what model should I use?"
• Business System Expertise - Familiarity with the landscape of enterprise business systems, CRM (Salesforce), marketing automation (Marketo), support platforms (Zendesk), integration and orchestration tools (Workato), AI platforms (Relevance AI), and enterprise search and knowledge tools (Glean). You don't need deep experience with all of these, but to understand what they do, how they fit together, and be willing to build with and across them. A strong understanding of enterprise data models and workflows is essential.
• Broad Functional Understanding - Ability to have meaningful conversations with stakeholders across diverse domains and quickly understand their unique needs.
• End-to-End Ownership - Track record of owning complex initiatives from discovery through delivery. Comfortable operating with ambiguity and driving to measurable outcomes independently.
• Product Mindset - Ability to scope MVPs, prioritise ruthlessly, and deliver iteratively. In addition, consider adoption, user experience, and business outcomes.
Preferred requirements
任职要求
• Background in consulting, solutions engineering, or customer-facing technical roles
• Familiarity with value stream mapping, flow metrics, or Theory of Constraints thinking
• Experience with low-code/no-code orchestration tools (n8n, Make, Workato) alongside custom development
• Previous startup or high-growth company experience
福利待遇
• Flexible Paid Time Off
• Team Member Resource Groups
• Equity Compensation & Employee Stock Purchase Plan
• Growth and Development Fund
• Parental Leave
Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application.
Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process.
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