LLM Infrastructure Engineer
加入 Hire Hangar,并与快速增长的全球公司合作，同时建立一个长期职业生涯。工作标题：LLM 基础设施工程师位置 ◎远程时间区 ◎美国时区 (ESTPST) 角色概述：我们正在寻找具有实践经验的 LLM 基础架构工程师来建造推动大型语言模型的系统。这是专业的实践工程角色，重点是设计，部署和扩展模型培训，细节调整和推断背后的基础设施。你将密切地与机器学习和工程团队合作以确保模型在生产中运行得可靠，高效和成本效率。理想的候选人了解 LLM 工作负载以及需要支持规模化的云系统的要求。主要责任：- 设计，构建和维护培训，细节调整和服务大型语言模型的基础设施 - 将部署并扩展 LLM 推断服务，以关注延迟，吞吐量和可靠性 - 管理 GPU 计算资源并优化工作负荷安排和利用 - 建立和维护数据管道来支持模型培训和评估 - 实施监测，记录和对模型性能和系统健康进行警报 - 在保持性能和可用性同时优化基础设施成本 - 通过基础设施作为代码和 CI/CD 实践自动化部署工作流程 - 与 ML 工程师和研究人员合作，将模型从试验转移到生产中 - 主动地确定瓶，调查根本原因并解决基础设施问题 要求的资格：- 经验证明建立大型语言模型基础设施 （不可谈判）- 在生产环境中实际部署和服务机器学习体验模型 - 有在系统或后端开发方面的经验的 Python 编程技能强大 - 用 Docker 和 Kubernetes 等容器和调整工具的经验 - 强有力的写和说英语，具有明确的专业沟通风格 - 必须拥有先前远程工作经验，熟练使用远程协作工具和平台 （如 Slack,Zoom,Google Workspace,Asana 等）,并且理想情况下与美国或英国公司合作。没有这种经验的申请将不考虑。优先资格：- 强大的 AWS 经验，包括此类服务
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Job Title: LLM Infrastructure Engineer
Location | Remote
Time Zone | US Time Zones (EST–PST)
Role Overview:
We are seeking an LLM Infrastructure Engineer with hands-on experience building the systems that power large language models. This is a full-time, hands-on engineering role focused on designing, deploying, and scaling the infrastructure behind model training, fine-tuning, and inference. You will work closely with machine learning and engineering teams to make sure models run reliably, efficiently, and cost-effectively in production. The ideal candidate understands both the demands of LLM workloads and the cloud systems needed to support them at scale.
Key Responsibilities:
- Design, build, and maintain infrastructure for training, fine-tuning, and serving large language models
- Deploy and scale LLM inference services with a focus on latency, throughput, and reliability
- Manage GPU compute resources and optimize workload scheduling and utilization
- Build and maintain data pipelines that support model training and evaluation
- Implement monitoring, logging, and alerting for model performance and system health
- Optimize infrastructure costs while maintaining performance and availability
- Automate deployment workflows using infrastructure-as-code and CI/CD practices
- Collaborate with ML engineers and researchers to move models from experimentation into production
- Proactively identify bottlenecks, investigate root causes, and resolve infrastructure issues
Required Qualifications:
- Proven experience building large language model infrastructure (non-negotiable)
- Hands-on experience deploying and serving machine learning models in production environments
- Strong programming skills in Python, with experience in systems or backend development
- Experience with containerization and orchestration tools such as Docker and Kubernetes
- Solid understanding of distributed systems, GPU computing, and performance optimization
- Strong written and spoken English with a clear, professional communication style
- Must have prior remote work experience, be fluent with remote collaboration tools and platforms (such as Slack, Zoom, Google Workspace, Asana, or similar), and have ideally worked with US or UK-based companies. Applications without this experience will not be considered.
Preferred Qualifications:
- Strong AWS experience, including services such as EC2, EKS, S3, and SageMaker
- Prior experience working at a large company or within enterprise-scale engineering environments
- Familiarity with LLM serving frameworks such as vLLM, TensorRT-LLM, or Triton Inference Server
- Experience with infrastructure-as-code tools such as Terraform or CloudFormation
- Experience with distributed training frameworks such as PyTorch Distributed, DeepSpeed, or Ray
Tools & Technology:
- AWS
- Python
- Docker and Kubernetes
- Terraform or CloudFormation
- PyTorch and LLM serving frameworks
- CI/CD and monitoring tools
- Slack, Zoom, and other remote collaboration tools
Please NOTE:
It is crucial that you complete the application form in full. As part of the application process, you will be required to record a video. If your application is successful, you will receive an email confirming next steps—the video is the first step of the interview process. If you do not record a video, we will not be able to consider you for ANY open roles.
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