LLM Engineer

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LLM 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: LLM Engineer Location: 100% Remote (U.S.) Position Type: Full-time, Direct W2 Salary Range: $100,000–$150,000 Annually Experience Required: 10+ 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 looking for an LLM Engineer to design, execute, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches. The role requires deep practical experience with modern training stacks, careful dataset construction, rigorous evaluation methodology, and the engineering discipline to operate complex training pipelines reliably. The ideal candidate combines strong ML intuition with production-grade engineering practices, and is comfortable navigating the trade-offs between data quality, compute budget, evaluation rigor, and shipping velocity. In this role you will work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions, and will be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production. Key Responsibilities Design and execute fine-tuning experiments for large language models using supervised, DPO, RLHF, and related techniques. Lead dataset construction, curation, and quality assurance processes for instruction tuning and preference data. Build scalable training pipelines on top of modern distributed training frameworks. Tune hyperparameters, optimizer configurations, and training stability strategies for large-model fine-tuning. Implement parameter-efficient fine-tuning techniques such as LoRA, QLoRA, and adapter-based methods. Design rigorous evaluation suites including automated benchmarks, human evaluation, and capability-specific probes. Implement safety, refusal, and policy evaluations to track model behavior across releases. Operate large-scale training jobs on GPU clusters, diagnosing failures and recovering training state reliably. Optimize training throughput using mixed precision, sequence packing, and efficient attention implementations. Manage model artifacts, lineage tracking, and reproducibility across many concurrent experiments. Collaborate with product, research, and platform teams to align fine-tuning roadmaps with business needs. Document training methodology, results, and decisions clearly for technical and non-technical audiences. Mentor engineers on fine-tuning best practices, evaluation rigor, and responsible deployment. Stay current with LLM research and translate advances into production-ready fine-tuning recipes. Required Qualifications Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent experience. Ten or more years of combined ML research and engineering experience, with significant LLM exposure. Strong proficiency in Python and modern deep learning frameworks, especially PyTorch. Hands-on experience fine-tuning transformer-based language models at non-trivial scale. Familiarity with distributed training strategies including FSDP, ZeRO, and pipeline parallelism. Experience with RLHF, DPO, or other preference optimization techniques. Strong understanding of evaluation methodology, benchmarks, and human evaluation design. Experience operating training jobs on GPU clusters and reco

LLM工程师 - 远程

Bright Vision Technologies是一家技术咨询和软件开发公司，在美国各地提供云、AI、数据和企业解决方案。这是一个绝佳的机会，可以加入一家成熟且备受尊敬的机构，获得巨大的职业发展潜力。

职位名称：LLM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$100,000–$150,000
所需经验：10年以上
担保：鼓励美国公民、绿卡持有者、EAD持有者和H-1B转移候选人申请。我们无法为该职位担保新的H-1B签证申请。

职位摘要：
我们正在寻找一名LLM工程师，负责设计、执行和运营大型语言模型的微调工作流，涵盖监督式、基于偏好和强化学习方法。该角色要求在现代训练栈方面具有深厚的实践经验、细致的数据集构建、严格的评估方法，以及可靠运行复杂训练管道的工程纪律。理想的候选人将强大的ML直觉与生产级工程实践相结合，并能够自如地在数据质量、计算预算、评估严谨性和交付速度之间进行权衡。

在该角色中，您将与跨职能合作伙伴——产品、设计、工程、运营和业务利益相关者——密切合作，将模糊的需求转化为精心设计的解决方案，并期望通过代码审查、设计审查和对初级工程师的指导来提升标准。成功的候选人具备强大的工程纪律、清晰的沟通风格，以及在交付有意义的工作方面拥有良好记录，这些工作在生产中表现良好。

主要职责
使用监督式、DPO、RLHF及相关技术设计并执行大型语言模型的微调实验。
领导指令调优和偏好数据的数据集构建、整理和质量保证流程。
在现代分布式训练框架之上构建可扩展的训练管道。
为大型模型微调调整超参数、优化器配置和训练稳定性策略。
实现参数高效微调技术，如LoRA、QLoRA和基于适配器的方法。
设计严格的评估套件，包括自动化基准、人工评估和能力特定探测。
实施安全性、拒绝和政策评估，以跟踪各发布版本的模型行为。
在GPU集群上运行大规模训练作业，诊断故障并可靠地恢复训练状态。
使用混合精度、序列打包和高效注意力实现来优化训练吞吐量。
管理模型工件、血缘跟踪以及众多并发实验的可复现性。
与产品、研究和平台团队合作，使微调路线图与业务需求保持一致。
为技术和非技术受众清晰记录训练方法、结果和决策。
指导工程师了解微调最佳实践、评估严谨性和负责任部署。
紧跟LLM研究，将进展转化为生产就绪的微调配方。

所需资格
计算机科学、机器学习或相关领域的硕士或博士学位；或同等经验。
十年或以上ML研究和工程经验的组合，具有显著的LLM接触经验。
精通Python和现代深度学习框架，尤其是PyTorch。
具有在非平凡规模下微调基于Transformer的语言模型的实践经验。
熟悉分布式训练策略，包括FSDP、ZeRO和管道并行。
具有RLHF、DPO或其他偏好优化技术的经验。
对评估方法、基准和人工评估设计有深入理解。
具有在GPU集群上运行训练作业和恢复的经验。

⚑ 岗位信息收录自公开招聘页（Himalayas），以雇主官网为准