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Chaos Engineering Jobs (NOW HIRING)

Data Scientist, AI/ML

$220K - $290K/yr

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 ...

$44 - $58.50/hr

Establish a continuous chaos engineering and resilience testing practice through fault injection, game days, and controlled failure experiments. * Mentor senior and mid-level engineers while raising ...

... chaos engineering. Responsibilities Own the enterprise Performance Engineering charter as a horizontal shared service; define engagement models, intake/prioritization, and performance-readiness ...

Execute chaos engineering experiments to identify system weaknesses; contribute to frameworks for safe production testing * Participate in game day exercises and disaster recovery simulations; create ...

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Chaos Engineering information

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$46.5K

$146.9K

$174K

How much do chaos engineering jobs pay per year?

As of Aug 20, 2026, the average yearly pay for chaos engineering in the United States is $146,868.00, according to ZipRecruiter salary data. Most workers in this role earn between $116,500.00 and $173,000.00 per year, depending on experience, location, and employer.

What is chaos engineering?

A Chaos Engineering job involves proactively identifying weaknesses in complex systems by intentionally injecting failures and observing how they respond. Professionals in this role design and execute controlled experiments to improve system resilience, ensuring that services remain reliable under unexpected conditions. They work closely with development, operations, and security teams to enhance fault tolerance and incident response strategies.

What are some typical challenges a chaos engineer faces, and how do they overcome them?

Chaos Engineers often face the challenge of designing effective experiments that simulate real-world failures without disrupting production systems. Balancing the need to discover vulnerabilities with maintaining uptime requires careful planning, communication, and coordination with development and operations teams. They address these challenges by thoroughly testing in controlled environments, documenting procedures, and establishing clear rollback strategies. Continuous learning and cross-functional collaboration are also key to staying ahead of new complexities in evolving systems.

What are the key skills and qualifications needed to thrive in the chaos engineering position, and why are they important?

To thrive in Chaos Engineering, a strong background in software engineering, distributed systems, and reliability testing is essential, often supported by a degree in computer science or a related field. Familiarity with chaos engineering tools like Gremlin or Chaos Monkey and experience with cloud platforms, container orchestration, and monitoring systems are highly valued. Excellent problem-solving abilities, communication skills, and a mindset oriented toward experimentation help engineers collaborate effectively and analyze complex failure modes. These skills are crucial for proactively identifying system weaknesses and ensuring the resilience of large-scale technology infrastructures.

Is chaos engineering still relevant?

Chaos engineering is a valuable practice for proactively identifying system vulnerabilities by intentionally introducing failures. It remains relevant in modern DevOps and cloud environments to improve system resilience and reliability, often utilizing tools like Chaos Monkey and Gremlin. As systems grow more complex, the need for chaos engineering skills continues to increase for engineers focused on fault tolerance and system stability.

What does a chaos engineer do?

A chaos engineer designs and executes experiments to intentionally disrupt systems in order to identify vulnerabilities and improve resilience. They use tools like chaos engineering frameworks to simulate failures and ensure systems can withstand unexpected issues, often working closely with development and operations teams. Strong knowledge of distributed systems, scripting, and monitoring is essential for this role.
More about Chaos Engineering jobs

What cities are hiring for Chaos Engineering jobs?

Cities with the most Chaos Engineering job openings:

What are the most commonly searched types of Chaos Engineering jobs?

The most popular types of Chaos Engineering jobs are:

What states have the most Chaos Engineering jobs?

States with the most job openings for Chaos Engineering jobs include:

Infographic showing various Chaos Engineering job openings in the United States as of August 2026, with employment types broken down into 50% Full Time, and 50% Contract. Highlights an 100% In-person job distribution, with an average salary of $146,868 per year, or $70.6 per hour.

$220K - $290K/yr

Full-time

Retirement

Re-posted 6 days ago


Job description

Data Scientist, AI/MLJob 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 - https://www.gremlin.com/about