Data Scientist, AI/ML

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🇺🇸 United States – Remote

💵 $220k - $290k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

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Gremlin

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2016

☁️ SaaS

🏢 Enterprise

🤝 B2B

SaaS • Enterprise • B2B

Gremlin is a company dedicated to enhancing enterprise software reliability. It offers an enterprise reliability platform with features like reliability management, chaos engineering, and fault injection to help organizations identify and mitigate reliability risks. Gremlin empowers clients by finding and fixing potential failures and scoring system reliability to enhance resilience and minimize downtime. By supporting industries like finance and retail, Gremlin aims to build trust in complex systems and modernize reliability practices, making them more robust and dependable.

📋 Description

• 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

🎯 Requirements

• 5+ years professional experience building and productionizing machine learning • 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 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

🏖️ Benefits

• Competitive total compensation packages including 401k Matching • Equity • Flexible time off • Paid company holidays