Data Scientist, AI/ML

• 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.