AI Pipeline Engineer

Company: Bright Vision Technologies

Location: Issaquah, WA (Remote)

Type: Full-time

Remote: Yes

Posted: 2026-09-04

About this role

- 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:
AI Pipeline Engineer
Location
: 100% Remote (U.S.)


Position
Type
: Full-time, Direct W2


Salary Range
: $100,000–$150,000 Annually


Experience
Required
: 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 AI Pipeline 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-...

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