Remote ML Data Engineer — Scale Petabyte Pipelines

Bright Vision Technologies; San Diego, 4S Ranch (CA, CA); Remote; USD 80,000 - 100,000; Full time

Job summary

Bright Vision Technologies is seeking a Machine Learning Data Engineer to build and operate large-scale data systems powering AI training and evaluation pipelines.

The role combines deep data engineering with an understanding of AI workloads, focusing on ingestion, transformation, quality assurance, lineage, and high-throughput data delivery to training jobs across diverse modalities. The ideal candidate has petabyte-scale data systems experience and strong software engineering fundamentals.

Qualifications

• Bachelor’s or Master’s degree in Computer Science or related field.
• 6+ years of data engineering experience, with ML/AI workloads.
• Strong Python and at least one JVM or systems language.
• Experience with Spark, Ray, or Beam.
• Hands-on experience with petabyte-scale storage and pipelines.
• 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

Distributed systems

Data engineering

Big data frameworks

Education

Bachelor’s or Master’s degree in Computer Science or related field

Tools

Spark

Ray

Beam

CI/CD

Job description

Bright Vision Technologies is seeking a Machine Learning Data Engineer to build and operate large-scale data systems powering AI training and evaluation pipelines.

The role combines deep data engineering with an understanding of AI workloads, focusing on ingestion, transformation, quality assurance, lineage, and high-throughput data delivery to training jobs across diverse modalities. The ideal candidate has petabyte-scale data systems experience and strong software engineering fundamentals.