高级机器学习工程师
Senior Machine Learning Engineer
关于公司
我们的客户是一家由东南亚领先科技公司支持的隐秘人工智能初创公司，目前正在组建其全球创始团队。
该公司正在开发一个以人工智能为核心的通信平台，旨在通过将人工智能直接整合到对话中，简化日常任务。用户无需在多个应用程序之间切换，即可在一个智能助手内完成计划、组织、比较、研究和完成任务。
面向仍依赖传统生产力工具的数十亿用户市场，该平台专注于提供可靠的AI工作流程、持久的上下文理解、多步骤推理和无缝的任务执行。其使命是打造一个显著提升生产力的AI助手，使日常工作的效率更高、更直观。
关于职位
我们的客户正在寻找一名高级机器学习工程师，负责设计和构建支撑其AI平台的生产级机器学习系统。这是一项注重实际技术的职位，专注于解决复杂的机器学习挑战，将研究成果转化为可扩展的解决方案，并交付能够在真实环境中有效运行的可靠AI系统。
与研究、产品和工程团队紧密合作，该职位将独立负责关键机器学习组件的开发及生产部署。
主要职责
- 构建和开发支撑AI驱动产品功能的核心机器学习系统。
- 独立负责整个机器学习生命周期，包括数据准备、模型训练、评估、推理、部署和持续迭代。
- 将研究概念转化为可靠、可生产的机器学习解决方案。
- 利用真实世界的运营洞察，调查并解决模型性能问题和生产系统故障。
- 通过快速实验、测量和迭代持续改进机器学习系统。
- 与研究、产品和工程团队密切合作，交付有影响力的AI功能。
- 通过代码审查、最佳实践和对其他机器学习工程师的指导提供技术支持。
- 开发兼顾现实生产需求的解决方案，包括延迟、可扩展性、成本效率、可靠性和安全性。
要求
- 具备构建和部署被真实用户使用的生产级机器学习系统的实际经验。
- 对...
查看英文原文
About the Company
Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team.
The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant.
Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive.
About the Role
Our client is seeking a Senior Machine Learning Engineer to design and build production-grade machine learning systems that power its AI platform. This is a hands-on technical role focused on solving complex machine learning challenges, translating research into scalable solutions, and delivering reliable AI systems that operate effectively in real-world environments.
Working closely with research, product, and engineering teams, this position will independently own critical machine learning components from development through production deployment.
Key Responsibilities
- Build and develop core machine learning systems that power AI-driven product capabilities.
- Own the end-to-end machine learning lifecycle, including data preparation, model training, evaluation, inference, deployment, and continuous iteration.
- Translate research concepts into reliable, production-ready machine learning solutions.
- Investigate and resolve model performance issues and production system failures using real-world operational insights.
- Continuously improve machine learning systems through rapid experimentation, measurement, and iteration.
- Collaborate closely with research, product, and engineering teams to deliver impactful AI features.
- Provide technical guidance through code reviews, best practices, and mentorship of fellow machine learning engineers.
- Develop solutions that balance real-world production requirements, including latency, scalability, cost efficiency, reliability, and safety.
Requirements
- Proven experience building and deploying production machine learning systems used by real users.
- Strong understanding of modern machine learning models, including their behavior, limitations, and failure modes in production environments.
- Excellent software engineering skills with the ability to develop robust, maintainable, production-quality systems.
- Experience designing and implementing scalable machine learning pipelines and infrastructure.
- Strong ownership mindset with the ability to independently solve complex technical challenges from concept through deployment.
- Excellent problem-solving, communication, and collaboration skills within cross-functional engineering teams.
- Demonstrated ability to iterate quickly and continuously improve machine learning systems based on production feedback.
Preferred Technical Skills
Experience with the following technologies is preferred:
- Python
- PyTorch and/or JAX
- GPU-based training and inference systems
Originally posted on Himalayas