Senior Data Engineer
Confirmed on the employer's own hiring board on Sep 28, 2026. First seen by Alion on Mar 27, 2026.
Senior Data Engineer - AI Systems
Employment Type: Full-time
Company: Permute (www.permute.ai)
Overview
Permute is seeking a Senior Data Engineer to design, build, and operate the data infrastructure that powers production AI systems. This role is for builders who move quickly, think clearly in ambiguous situations, and take ownership of turning messy data and evolving requirements into reliable, scalable systems.
We care as much about how you think and build as we do about your background. The ideal candidate is someone who can reason through new data problems, prototype quickly, and build robust pipelines and infrastructure in a fast-moving startup environment.
Responsibilities
Design and build scalable data pipelines and infrastructure supporting AI and ML systems
Develop and maintain data ingestion, transformation, and storage systems for large-scale datasets
Build data platforms that support LLM systems, agent workflows, and model evaluation
Develop tooling for data quality, validation, monitoring, and observability
Collaborate with engineering, product, and ML teams to deliver data systems that power AI features
Design systems that enable efficient experimentation, training, and evaluation of models
Optimize performance, reliability, and scalability of data infrastructure
Operate effectively in ambiguous, rapidly evolving startup environments
Required Qualifications
Strong background in data engineering and distributed systems
Experience building scalable data pipelines and data platforms
Strong foundation in algorithms, statistics, and data modeling
Proficiency with Python and modern data infrastructure tools
5+ years building and operating systems in AWS environments
Preferred Background
Degree in Mathematics, Physics, Computer Science, or a related technical field
Experience with:
End-to-end production data platforms
Streaming and batch data processing systems
Data infrastructure supporting ML training, MLOps, evaluation, and deployment
Data quality, monitoring, and observability systems
Infrastructure supporting AI or LLM-powered applications