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# Infrastructure Reliability Engineer

- **Company:** Bright Vision Technologies
- **Location:** Austin, TX, United States (Remote available)
- **Experience:** Expert
- **Salary:** $125,000.0 - $170,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Code Review, Continuous Integration, Data Cleansing, Data Deduplication, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Distributed Systems, Document-Oriented Databases, Java Virtual Machine (JVM), Python (Programming Language), Machine Learning, Open Source Technology, Azure Machine Learning, Software Engineering, System on a Chip, Management of Software Versions, Data Processing, Apache Spark, Caching, Storage Technologies, Information Technology, Integration Frameworks, Free and Open-Source Software, Machine Learning Operations, Data Pipelines
- **Published:** September 26, 2026
- **Apply:** https://www.careerjet.com/jobad/us28cc6d4754b916c3ea5a3bdf24170115

## About the Role

This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential., * Bachelor's or Master's degree in Computer Science or a related field.

 * Six or more years of data engineering experience, with significant work supporting ML or AI workloads.
 * Strong proficiency in Python and at least one JVM or systems language.
 * Deep experience with modern data processing frameworks such as Spark, Ray, or Beam.
 * Hands-on experience operating petabyte-scale storage and pipeline systems.
 * Strong understanding of distributed systems, data modeling, and storage formats.
 * Experience with dataset versioning, lineage, and reproducibility for ML workflows.
 * Familiarity with high-throughput data loading for accelerator-based training.
 * Strong software engineering practices including testing, CI/CD, and code review.
 * Excellent communication and cross-functional collaboration skills., * Experience with multimodal datasets at large scale.
 * Familiarity with data quality tooling and dataset evaluation methodology.
 * Exposure to privacy-preserving data systems and regulated data handling.
 * Open-source contributions to data infrastructure projects.
 * Experience supporting frontier model training pipelines.

## Description

We are seeking an Infrastructure Reliability Engineer to build and operate the large-scale data systems that power modern AI training and evaluation pipelines. The role combines deep data engineering expertise with a strong understanding of AI workloads, focusing on ingestion, transformation, quality assurance, lineage, and high-throughput delivery of data to training jobs across diverse modalities. The ideal candidate has experience operating petabyte-scale data systems, strong software engineering fundamentals, and clear understanding of how data infrastructure choices propagate into model quality and training efficiency., * Design and operate large-scale data pipelines supporting AI training, evaluation, and continual improvement workflows.

 * Build ingestion systems for diverse modalities including text, image, audio, video, and structured signals.
 * Implement data cleaning, deduplication, filtering, and quality assurance at petabyte scale.
 * Develop dataset versioning, lineage, and provenance tracking systems suitable for reproducible training.
 * Build high-throughput data loading systems that maximize GPU utilization during training.
 * Implement labeling workflows, active learning pipelines, and human-in-the-loop data improvement systems.
 * Design storage architectures balancing cost, throughput, and latency across data tiers.
 * Build evaluation dataset construction pipelines with strict integrity and contamination controls.
 * Implement data privacy, redaction, and consent enforcement throughout the pipeline.
 * Collaborate with ML researchers and engineers to align data systems with model development needs.
 * Drive observability of data quality, drift, and pipeline health across the AI data estate.
 * Optimize cost and performance through compression, format selection, and caching strategies.
 * Document data systems, schemas, and operational procedures for broad internal use.
 * Stay current with AI data infrastructure research and emerging open-source tools., Custom SoCs (System on Chip) live at the heart of AWS Machine Learning servers. As a member of the Cloud-Scale Machine Learning Acceleration team you'll be responsible for the desi…
 + 14 days ago