出海雷达

数据科学家,AI/ML

Data Scientist, AI/ML

AI限定地区(需当地身份)
公司Gremlin
薪资$220,000 - $290,000/年
工作地点United States
地域资格限定地区(需当地身份)
时区要求日间重叠约 9 小时,基本正常作息
用工类型Full Time
发布时间2 天前
数据来源Himalayas
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注意地域限制:该职位明确限定在 United States 招聘。如果你是位于中国大陆的求职者,通常需要当地工作身份才能投递,或需与雇主确认是否接受独立合同(Contractor)形式合作。

数据科学家,AI/ML
职位描述:
如今复杂的、快节奏的系统已成为可靠性的雷区,其中任何一个问题都可能导致造成数百万损失并破坏客户信任的停机事件。这就是为什么高可用性团队使用 Gremlin 来在故障发生前发现并修复可靠性风险。
Gremlin 可靠性平台帮助软件团队主动监控和测试系统以识别常见的可靠性风险,建立并执行可靠性标准,并在整个组织中自动化可靠性实践。作为混沌工程和可靠性测试领域的行业领导者,我们与全球数百家大型组织合作,这些组织对高可用性有不可妥协的要求。
数据科学家,AI/ML 职位简介
在 Gremlin 担任数据科学家,AI/ML 的角色,你将有机会通过将数百万次混沌工程实验转化为自动化的故障分析和修复,从而提升整个互联网的可靠性。你将能够利用你在应用机器学习方面的经验来指导产品方向,并解决直接影响我们客户的复杂技术问题(客户范围从财富 500 强到小型组织)。你将与一支小型、才华横溢的工程团队紧密合作,该团队专注于质量、交付和可预测性,并特别注重为客户提供出色的用户体验。
在这一职位中,你将负责:

  • 分析 Gremlin 自有的包含数百万次混沌工程实验的数据集,以识别复杂分布式系统中的故障模式、根本原因和弹性信号
  • 预训练和微调机器学习模型,以自动检测、分类和解释混沌实验中观察到的故障
  • 构建智能系统,通过学习历史实验结果和系统行为,提供自动化的修复建议,最终实现编排
  • 开发可扩展的数据管道和特征存储,以处理、丰富和提供大量实验数据,用于模型训练和实时推理
  • 与平台工程师和 SRE 紧密合作,将基于 AI 的故障分析和修复功能直接集成到 Gremlin 核心产品中
  • 应用高级技术,包括因果推断、图机器学习、时间序列建模和强化学习,以持续提高自动化故障分析的准确性和可操作性
  • 将洞察力转化为实际成果
查看英文原文

Data Scientist, AI/ML
Job Description:
Today’s complex, fast-paced systems have become a minefield of reliability risks, any of which could cause an outage that costs millions and destroys customer confidence. That’s why high-availability teams use Gremlin to find and fix reliability risks before they become incidents.
Gremlin Reliability Platform helps software teams proactively monitor and test their systems for common reliability risks, build and enforce reliability standards, and automate their reliability practices organization-wide. As the industry leader in Chaos Engineering and reliability testing, we work with hundreds of the world’s largest organizations where high availability is non-negotiable.
About the Role of the Data Scientist, AI/ML
As a Data Scientist, AI/ML at Gremlin, you will have the opportunity to improve the reliability of the internet at large by turning millions of chaos engineering experiments into automated failure analysis and remediation. You will be able to leverage your applied machine learning experience to inform product direction as well as solve complex technical problems that directly impact our customers (which range from the Fortune 500 to smaller organizations). You will work closely with a small, talented engineering team focused on quality, delivery, and predictability with an emphasis on providing our customers a great user experience.
In this role, you’ll get to:

  • Analyze Gremlin’s proprietary dataset of millions of chaos engineering experiments to identify failure patterns, root causes, and resilience signals across complex distributed systems
  • Pretraining and fine-tuning machine learning models that automatically detect, classify, and explain failures observed during chaos experiments
  • Build intelligent systems that deliver automated remediation recommendations, and eventually orchestration, by learning from historical experiment outcomes and system behavior
  • Develop scalable data pipelines and feature stores to process, enrich, and serve large volumes of experiment data for both model training and real-time inference
  • Collaborate closely with platform engineers and SREs to integrate AI-driven failure analysis and remediation capabilities directly into Gremlin’s core product
  • Apply advanced techniques, including causal inference, graph ML, time-series modeling, and reinforcement learning, to continuously improve the accuracy and actionability of automated failure analysis
  • Translate insights from millions of chaos experiments into AI-powered features that help customers automatically understand blast radius, pinpoint root causes, and accelerate recovery
  • Research and productionize novel ML approaches, including causal AI and agentic systems, that turn raw chaos experiment data into automated, reliable remediation strategies

We’ll expect you to have:

  • Experience as a self-driven and collaborative problem solver with strong communication skills
  • 5+ years professional experience building and productionizing machine learning, ideally for distributed systems, infrastructure, or DevOps and SRE use cases with more overall years of experience in software development.
  • Hands-on experience with techniques such as causal inference, graph ML, time-series modeling, or reinforcement learning
  • Experience building data pipelines and feature stores that support both offline training and real-time inference
  • Experience with agile development environments and practices
  • Strong advocate and practitioner of rigorous experimentation, model evaluation, and engineering best practices
  • Comfort partnering with platform engineers and SREs to turn research into shipped product features
  • Strong at breaking down ambiguous problems into concrete actions and milestones

Bonus Experience:

  • Experience with chaos engineering, site reliability engineering, or distributed systems
  • Background in agentic AI systems or large-scale causal inference in production
  • Experience standing up MLOps tooling such as model serving, monitoring, or feature store infrastructure
  • Working in Remote first environments
  • Has been on-call and participated in an incident management program

*The role does not offer sponsorship employment benefits.
**If you don’t think you meet all of the criteria above but still are interested in the job, please apply. Nobody checks every box, we’re looking for candidates that are particularly strong in a few areas, and have some interest and capabilities in others.
Compensation
We expect the salary range for this role to be $220,000 - $290,000. We recognize that salary varies from person to person depending on level of experience and we welcome direct conversations about it. The final offer will vary based on assessment of a candidate's skills and ability and our budget and market data.
Gremlin offers competitive total compensation packages including 401k Matching, Equity and other benefits such as flexible time off and paid company holidays.
About Gremlin:
Gremlin is a team of industry veterans and people eager to learn from one another. We set the standard for reliability and equip leading organizations with the mindset and expertise needed to drive reliability improvements that move the world forward. We’re backed by top-tier investors Index Ventures, Amplify Partners, and Redpoint Ventures. Our customers love us, and we’re thrilled to be a partner in their success.
What Do We Care About:
We Care about our People
People are our critical differentiators. The company strives to treat our people with respect, empathy, and dignity. We expect that our people will treat each other similarly. In both cases, we will assume good intent. All are welcome at Gremlin. We know our differences make us stronger and that our best ideas and contributions can come from anyone at any level.
We Care about Collaboration
Gremlin is strongest when we come together as one team with shared goals. Be the glue, not the glitter. But as a remote company, teamwork and collaboration won’t happen by accident. We approach every challenge as a shared challenge. We rely on each other for diverse perspectives and creative ideas. We celebrate our wins as a team.
We Care about Results
Be high productivity, low drama. Results matter. To keep our pace, everyone owns the outcomes of their actions and takes action when needed. We reward speed over perfection. We empower each other to iterate and experiment. You are welcome at Gremlin for who you are. The more voices and ideas we have represented in our business, the more we will all flourish, contribute, and build a more reliable internet.
Gremlin is a place where everyone can grow and is encouraged. However you identify and whatever background you bring with you, please apply if this sounds like a role that would make you excited to come into work everyday. It’s in our differences that we will find the power to keep building a more reliable internet by building and designing tools used by the best companies in the world.
Visit our website to learn more -
Originally posted on Himalayas

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