基础设施可靠性工程师
Infrastructure Reliability Engineer
基础设施可靠性工程师 – 远程办公
Bright Vision Technologies 是一家技术咨询和软件开发公司，为美国各地提供云、AI、数据和企业解决方案。加入一家成熟且备受尊敬的组织，这是一个绝佳的机会，提供巨大的职业发展潜力。
职位名称：基础设施可靠性工程师
工作地点：100% 远程（美国）
职位类型：全职，直接 W2
薪资范围：每年 125,000–170,000 美元
所需经验：6 年以上
职位简介
我们正在寻找一名基础设施可靠性工程师，负责构建和运营支撑现代 AI 训练和评估流程的大型数据系统。该职位结合了深厚的数据工程专业知识与对 AI 工作负载的深入理解，专注于数据摄取、转换、质量保障、数据血缘关系以及跨多种模态的训练任务的高吞吐量数据交付。理想的候选人具备运行 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型数据流水线。
- 构建支持多种模态（包括文本、图像、音频、视频和结构化信号）的数据摄取系统。
- 实现 PB 级数据清洗、去重、过滤和质量保障。
- 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、主动学习流水线和人机协同数据优化系统。
- 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流水线。
- 在整个数据流水线中实现数据隐私、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、漂移和流水线健康状况的可观测性。
- 通过压缩、格式选择和缓存策略优化成本和性能。
- 为内部广泛使用编写数据系统、模式和操作流程的文档。
- 跟踪最新技术并保持对行业趋势的了解。
查看英文原文
Infrastructure Reliability 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: Infrastructure Reliability Engineer
Location: 100% Remote (U.S.)
Position Type: Full-time, Direct W2
Salary Range: $125,000–$170,000 Annually
Experience Required: 6+ years
Job Summary
We are seeking an Infrastructure Reliability 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)676-4399. 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