AI/ML Reliability Scientist - Chaos Engineering (Remote)

Devconnectplatform; Northern (KY); Remote; USD 220,000 - 290,000; Full time

Benefits offered by this job

401k Matching

Equity

Flexible time off

Paid holidays

Job summary

Gremlin is seeking a Data Scientist, AI/ML to improve reliability by turning chaos experiments into automated failure analysis and remediation. You will work with a small engineering team focused on quality, delivery, and user experience, applying advanced ML to production-scale data.

You will build scalable pipelines, collaborate with SREs, and translate insights from millions of experiments into actionable features that help customers understand blast radius and accelerate recovery.

Qualifications

• 5+ years of professional experience in machine learning for distributed systems, infrastructure, DevOps, or SRE-focused roles.
• Proven ability to design and evaluate ML models in production settings and an appetite for rigorous experimentation.

Responsibilities

• Analyze Gremlin’s large chaos-experiment dataset to identify failure patterns and resilience signals.
• Pretrain and fine-tune ML models to automatically detect, classify, and explain failures.
• Develop automated remediation recommendations and orchestration from historical outcomes.
• Build scalable data pipelines and feature stores for offline training and real-time inference.
• Collaborate with platform engineers and SREs to embed AI-driven failure analysis into Gremlin’s core product.
• Apply causal inference, graph ML, time-series, and reinforcement learning to improve automated failure analysis.

Skills

Causal inference

Graph ML

Time-series modeling

Reinforcement learning

Data pipelines

Feature stores

Distributed systems

SRE collaboration

Tools

MLOps tooling

Model serving

Monitoring

Feature store infra

Job description

Gremlin is seeking a Data Scientist, AI/ML to improve reliability by turning chaos experiments into automated failure analysis and remediation. You will work with a small engineering team focused on quality, delivery, and user experience, applying advanced ML to production-scale data.

You will build scalable pipelines, collaborate with SREs, and translate insights from millions of experiments into actionable features that help customers understand blast radius and accelerate recovery.