Infrastructure Reliability Engineer
About The Position
Bright Vision Technologies is seeking an Infrastructure Reliability Engineer to build and operate large-scale data systems powering AI training and evaluation pipelines. The role combines data engineering expertise with an understanding of AI workloads, emphasizing ingestion, transformation, quality assurance, lineage, and high-throughput delivery of multimodal data to training jobs. The position calls for experience operating petabyte-scale data systems and an understanding of how data infrastructure affects model quality and training efficiency. This is a full-time, direct W2 position that is 100% remote within the United States. The annual salary range is $125,000–$170,000.
Requirements
- 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.
Nice To Haves
- 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.
Responsibilities
- Design and operate large-scale data pipelines for AI training, evaluation, and continual improvement workflows.
- Build ingestion systems for 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 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 that balance cost, throughput, and latency across data tiers.
- Build evaluation dataset construction pipelines with 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 internal use.
- Stay current with AI data infrastructure research and emerging open-source tools.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
