ML Data Engineer

**ML Data Engineer – Remote** 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 W2 **Salary 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. **Key Responsibilities** - Design and operate large-scale data pipelines supporting AI training, evaluation, and continual improvement workflows. - Build ingestion systems for diverse modalities including 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 systems suitable 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 balancing cost, throughput, and latency across data tiers. - Build evaluation dataset construction pipelines with strict 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 broad internal use. - Stay current with AI data infrastructure research and emerging open-source tools. **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. **How to Apply** Would you like to know more about this opportunity? For immediate consi…