ML Data Engineer Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.
Job Title: ML Data Engineer
Location: 100% Remote (United States)
Position Type: Full-time, Direct W2Salary Range: $100,000–$150,000 Annually
Experience: 6+ years
Sponsorship:
U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.
Job Summary:
We are seeking an ML Data 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. Required
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.
Preferred
Qualifications:
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.
