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Gremlin

Chaos engineering platform for reliability testing

Data Scientist - AI/ML

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote in USARemote

About the Job

Requirements

  • Experience as a self-driven and collaborative problem solver with strong communication skills.
  • At least 5 years of professional experience building and productionizing machine learning, ideally for distributed systems, infrastructure, or DevOps and site reliability engineering use cases, with more overall years of experience in software development.
  • Hands-on experience with causal inference, graph machine learning, 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.
  • Practice rigorous experimentation, model evaluation, and engineering best practices.
  • Comfort partnering with platform engineers and site reliability engineers to turn research into shipped product features.
  • Ability to break down ambiguous problems into concrete actions and milestones.

Responsibilities

  • Analyze Gremlin’s proprietary dataset of millions of chaos engineering experiments to identify failure patterns, root causes, and resilience signals across complex distributed systems.
  • Pretrain and fine-tune 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 model training and real-time inference.
  • Collaborate with platform engineers and site reliability engineers to integrate AI-driven failure analysis and remediation capabilities into Gremlin’s core product.
  • Apply causal inference, graph machine learning, time-series modeling, and reinforcement learning to improve the accuracy and actionability of automated failure analysis.
  • Translate insights from chaos experiments into AI-powered features that help customers understand blast radius, pinpoint root causes, and accelerate recovery.
  • Research and productionize machine-learning approaches, including causal AI and agentic systems, to turn chaos experiment data into automated remediation strategies.

Desired Qualifications

  • Experience with chaos engineering, site reliability engineering, or distributed systems.
  • Background in agentic AI systems or large-scale causal inference in production.
  • Experience setting up MLOps tooling such as model serving, monitoring, or feature-store infrastructure.
  • Experience working in remote-first environments.
  • Experience being on call and participating in an incident management program.

About the Company

Gremlin

Chaos engineering platform for reliability testing

Gremlin provides a platform that helps businesses, especially financial services firms, keep their software reliable and available by running controlled experiments that simulate failures (chaos engineering). The product works by letting teams design and run disruptions in their systems, measure how the software and infrastructure respond, and track changes over time, all through a subscription-based service with different tiers. What sets Gremlin apart is its industry focus on financial services, its emphasis on continuous testing of both applications and infrastructure, and its Enterprise Chaos Engineering Certification (GECEC) program, which trains and certifies professionals in chaos engineering and builds a community around reliability practices. The company’s goal is to reduce the risk of customer-facing outages and performance problems, helping customers meet high availability standards, protect revenue, and improve overall user experience.

Company size
51-200
Company stage
Series B
Total funding
$26.8M
Headquarters
San Jose, CA
Founded
2016

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