机器学习工程师 - AI
Machine Learning Engineer - AI
EGNYTE 你的职业发展，点燃你的热情
Egnyte 是一个为优秀人才创造机会的地方。我们相信每个职位都有其意义，每位 Egnyter 都应受到尊重。在全球拥有 23,000 家客户并持续增长的背景下，你可以通过保护他们的宝贵数据产生影响。加入 Egnyte 不仅仅是一份新工作，你将成为一个由实干者、思考者和合作者组成的团队的一员，他们践行并拥抱我们的价值观：
投资关系
财务审慎
坦诚对话
关于 Egnyte
Egnyte 是一个安全的多云内容安全与治理平台，使组织能够更好地保护和协作处理其最有价值的内容。自 2008 年成立以来，Egnyte 已为超过 23,000 家组织提供了民主化的云内容安全，帮助客户提升数据安全、保持合规、防止和检测勒索软件威胁，并在任何应用、任何云、任何地方提高员工生产力。更多信息，请访问。
你将负责：
- 使用 Hugging Face、TRL 和适配器方法（LoRA、QLoRA、PEFT）对 SLM 进行微调和训练
- 通过量化、剪枝和知识蒸馏优化模型以进行推理
- 在严格的延迟目标下将模型部署到边缘设备、移动端和本地服务器
- 从数据摄入到部署构建端到端 MLOps 流水线
- 监控生产环境中的模型准确性、延迟和硬件利用率
- 使用基准测试框架和自定义评估套件评估模型质量
你的资格要求：
- SLM 开发与微调：使用 Hugging Face 和适配器知识训练和微调 SLM
- 模型优化：应用量化、剪枝、知识蒸馏和优化以创建轻量高效的模型
- 边缘部署：将模型部署到边缘设备、移动端和本地服务器等
- 流水线工程：构建端到端 MLOps 流水线——从数据摄入到部署
- 性能监控：跟踪生产环境中的模型准确性、延迟和 CPU/GPU 使用情况
加分项：
- 在边缘或移动环境中的部署经验
- 了解 ONNX 导出和跨平台推理
- MLOps 工具——实验跟踪、模型注册表、ML 的 CI/CD
查看英文原文
EGNYTE YOUR CAREER. SPARK YOUR PASSION.
Egnyte is a place where we spark opportunities for amazing people. We believe that every role has meaning, and every Egnyter should be respected. With23,000 customers worldwide and growing, you can make an impact by protecting their valuable data. When joining Egnyte, you’re not just landing a new career, you become part of a team of Egnyterswhodoers, thinkers, and collaborators are who embrace and live by our values:
Invested Relationships
Fiscal Prudence
Candid Conversations
ABOUT EGNYTE
Egnyte is the secure multi-cloud platform for content security and governance that enables organizations to better protect and collaborate on their most valuable content. Established in 2008, Egnyte has democratized cloud content security for more than 23,000 organizations, helping customers improve data security, maintain compliance, prevent and detect ransomware threats, and boost employee productivity on any app, any cloud, anywhere. For more information, visit.
WHAT YOU’LL DO:
- Fine-tune and train SLMs using Hugging Face, TRL, and adapter methods (LoRA,QLoRA, PEFT)
- Optimize models for inference via quantization, pruning, and knowledge distillation
- Deploy models to edge devices, mobile, and local servers with strict latency targets
- Build end-to-endMLOpspipelines from data ingestion to deployment
- Monitor model accuracy, latency, and hardware utilization in production
- Evaluate model quality using benchmarking frameworks and custom evaluation suites
YOUR QUALIFICATIONS:
- SLM Development & Fine-tuning:Train and fine-tune SLMs using Hugging Face and Knowledge on Adaptors.
- Model Optimization:Apply quantization, pruning, knowledge distillation, and optimization for lightweight, efficient models.
- Edge Deployment:Deploy models to edge devices, mobile, and local servers, etc.
- Pipeline Engineering:Build end-to-endMLOpspipelines — from data ingestion to deployment.
- Performance Monitoring:Track model accuracy, latency, and CPU/GPU usage in production.
Good to have
- Deployment experience on edge or mobile environments
- Knowledge of ONNX export and cross-platform inference
- MLOpstooling — experiment tracking, model registries, CI/CD for ML
EQUAL EMPLOYMENT OPPORTUNITY
At Egnyte, we celebrate our unique differences and thrive on our diversity for our employees, our products, our customers, our investors, and our communities. Our global Egnyte Employee Communities (EECs) support representation and inclusion across our diverse workplace. Egnyters are encouraged to bring their whole selves to work and to appreciate the many differences that collectively make Egnyte a higher-performing company and a great place to be.
Any employees with questions or concerns about equal employment opportunities in the workplace are encouraged to bring these issues to the attention of . Egnyte will not allow any form of retaliation against employees who raise issues of equal employment opportunity. If employees feel they have been subjected to any such retaliation, they should contact . To ensure the workplace is free of artificial barriers, violation of this policy including any improper retaliatory conduct will lead to discipline, up to and including discharge. All employees must cooperate with all investigations conducted pursuant to this policy.
Originally posted on Himalayas