Machine Learning Engineer
职位标题：机器学习工程师位置：远程，全日制工作类型：全日制关于机会 我们的客户是领先的人工智能研究公司，正在寻找一个经验丰富的机器学习技师加入他们的"环境"团队。这是重点关注建立"模拟环境",其中他们的高级人工智能编码代理学会成为现实世界机器学习技术熟练。你将负责在大规模的第三方 ML 代码库之上构建复杂测试基础设施。你的工作将"评级"AI 代理的解决方案在困难任务上，如从研究论文中实现算法或调试培训框架。你还会构建自动化来加速测试和任务创建过程。这个角色需要一个能够以异常速度潜入新的，陌生的和高度复杂的 ML 代码库和框架的工程师。示例责任 · 为加强学习 (RL) 框架写一篇端到端测试以确保小模型的奖励功能按预期增加或检测数量不稳定性。· 找到提出模型培训的算法改进研究论文，实现它们 （在 PyTorch 或 Jax 中）,然后写一个广泛的测试套件来评估 AI 代理是否能够成功重新实施相同的算法。· 建立自动化工具以加速创建新的 ML 任务和测试管道。所需资格 ·经验：必须拥有多年的深入实践经验与 PyTorsch 或 JaX 等 ML 框架合作。· 追踪记录：使用大规模的多 GPU 环境的大数据集上训练大型模型的可证明的历史记录。· 主要属性：技术能力：在 PyTorch 或 Jax 中培训大型模型的经验详细介绍。最初发布于 Himalayas,主要是通过 PyTorch 和 Jax. 在此过程中，学生们可以进行两次面试，并获得一个简历的学习机会。
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Job Title: Machine Learning Engineer
Location: Remote, Full-Time
Job Type: Full-time
About the Opportunity
Our client, a leading AI research company, is seeking an experienced Machine Learning Engineer to join their "Environments" team. This is a critical infrastructure role focused on building the "simulation environments" where their advanced AI coding agents learn to become proficient in real-world Machine Learning engineering.
You will be responsible for building complex testing infrastructure on top of large-scale, third-party ML codebases. Your work will "grade" the AI agent's solutions on difficult tasks, such as implementing algorithms from research papers or debugging training frameworks. You will also build automations to accelerate the testing and task-creation process.
This role requires an engineer who can dive into new, unfamiliar, and highly complex ML codebases and frameworks with exceptional speed.
Example Responsibilities
- Write an end-to-end test for a Reinforcement Learning (RL) framework to ensure that a small model's reward function is increasing as expected or to detect numerical instabilities.
- Find research papers proposing algorithmic improvements for model training, implement them (in PyTorch or Jax), and then write an extensive test suite to evaluate whether an AI agent can successfully reimplement those same algorithms.
- Build automation tooling to speed up the creation of new ML tasks and testing pipelines.
Required Qualifications
- Experience: Must have multiple years of deep, hands-on experience working with ML frameworks like PyTorch or Jax.
- Track Record: A demonstrable track record of training large models on large datasets using large-scale, multi-GPU environments.
- Key Attribute: A proven, exceptional ability to learn new technologies and complex codebases at a very fast pace.
- Engineering Skills: Strong software engineering fundamentals, particularly in Python, and the ability to build robust, maintainable testing infrastructure.
Interview Process
The interview process is designed to be fast and decisive. It typically consists of 2-3 technical interviews, with a final decision and offer made within seven days in most cases.
How to Apply
Interested candidates are invited to submit their resume, detailing their experience in training large-scale models with PyTorch or Jax.
Originally posted on Himalayas