AI流程工程师
AI Pipeline Engineer
AI Pipeline Engineer- 远程办公
Bright Vision Technologies 是一家技术咨询和软件开发公司,为美国各地的客户提供云计算、人工智能、数据和企业解决方案。
这是一个加入一家知名且受人尊敬的组织的绝佳机会,提供巨大的职业发展潜能。
职位名称:AI Pipeline Engineer
地点:100% 远程(美国)
职位类型:全职,直接W2
薪资范围:每年10万至15万美元
所需经验:6年以上
赞助:美国公民、绿卡持有者、EAD持有者以及H-1B转签候选人欢迎申请。我们无法为该职位的新H-1B签证申请提供赞助。
职位概述
我们正在寻找一名AI Pipeline Engineer,负责构建和运营支撑现代AI训练和评估流程的大规模数据系统。该职位结合了深厚的数据工程专业知识与对AI工作负载的深刻理解,重点在于数据摄取、转换、质量保证、数据血缘关系以及跨多种模态的数据高吞吐量交付。理想的候选人具备操作PB级数据系统的经验,扎实的软件工程基础,并清楚了解数据基础设施选择如何影响模型质量和训练效率。
主要职责
· 设计和运营支持AI训练、评估和持续改进工作流的大规模数据管道。
- 构建针对多种模态(包括文本、图像、音频、视频和结构化信号)的摄取系统。
- 实现PB级的数据清洗、去重、过滤和质量保证。
- 开发适用于可重复训练的数据集版本控制、血缘关系和来源追踪系统。
- 构建高吞吐量的数据加载系统,以最大化训练期间的GPU利用率。
- 实现标注工作流、主动学习管道和人机协同的数据优化系统。
- 设计在数据层级之间平衡成本、吞吐量和延迟的存储架构。
- 构建具有严格完整性和污染控制的评估数据集构造管道。
- 在整个数据管道中实现数据隐私、脱敏和同意执行。
- 与ML研究人员和工程师合作,确保数据系统符合模型开发需求。
- 推动AI数据资产中数据质量、漂移和管道健康状况的可观测性。
- 通过压缩等方式优化成本和性能。
查看英文原文
AI Pipeline 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: AI Pipeline Engineer
Location: 100% Remote (U.S.)
PositionType: Full-time, Direct W2
Salary Range: $100,000–$150,000 Annually
ExperienceRequired: 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-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 consideration, please send your resume to or contact us at (908) 505-3544. Learn more about Bright Vision Technologies at .
Bright Vision Technologies is an Equal Opportunity Employer.
Equal Employment Opportunity (EEO) Statement
Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.
BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.
Originally posted on Himalayas