Data Scientist, Gremlin

• 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
• 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

Desirable

• 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
• 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