Remote ML Data Engineer — Petabyte-Scale Pipelines

Bright Vision Technologies

United States

Remote

USD 80,000 - 100,000

Full time

6 days ago
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Job summary

Bright Vision Technologies is seeking a Machine Learning Data Engineer to design and operate large-scale data systems powering AI training pipelines. The role emphasizes ingestion, transformation, quality assurance, lineage, and high-throughput data delivery across modalities. Ideal candidates will have experience with petabyte-scale data platforms and strong software engineering practices.

350-500? Example text placeholders removed to ensure accuracy; please replace with actual content if

Qualifications

  • Bachelor’s or Master’s degree in CS 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.
  • Experience 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

Python
JVM language
Distributed systems
Data modeling
Data pipelines
CI/CD
Code review
Testing
Communication

Education

Bachelor’s or Master’s in CS or related

Tools

Spark
Ray
Beam

Job description

Bright Vision Technologies is seeking a Machine Learning Data Engineer to design and operate large-scale data systems powering AI training pipelines. The role emphasizes ingestion, transformation, quality assurance, lineage, and high-throughput data delivery across modalities. Ideal candidates will have experience with petabyte-scale data platforms and strong software engineering practices.

350-500? Example text placeholders removed to ensure accuracy; please replace with actual content if

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