Remote ML Data Engineer - Scale-Power Data Pipelines

Bright Vision Technologies; Sterling (VA); On-site; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an ML Data Engineer to build and operate large-scale data systems powering AI training and evaluation pipelines. The role blends data engineering with AI workload awareness, focusing on ingestion, transformation, quality assurance, lineage, and high-throughput data delivery.

The ideal candidate has experience with petabyte-scale data platforms, strong software engineering practices, and a clear view of how data infrastructure choices impact model quality and

Qualifications

• 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.

Skills

Python

Java/Scala

Distributed systems

Data pipelines

CI/CD

Communication

Education

Bachelor’s or Master’s in Computer Science

Tools

Spark

Ray

Beam

Job description

Bright Vision Technologies is seeking an ML Data Engineer to build and operate large-scale data systems powering AI training and evaluation pipelines. The role blends data engineering with AI workload awareness, focusing on ingestion, transformation, quality assurance, lineage, and high-throughput data delivery.

The ideal candidate has experience with petabyte-scale data platforms, strong software engineering practices, and a clear view of how data infrastructure choices impact model quality and