Astrophysics Expert - Computational Specialist
关于工作的职位，默科尔将精英创意和技术人才与领先的人工智能研究实验室联系起来。总部位于旧金山，我们的投资者包括 Benchmark,General Catalyst,Peter Thiel,Adam D'Angelo,Larry Summers 和 Jack Dorsey.职位：计算天文学和宇宙学专家类型：合同补偿：$70$100/小时 地点：远程承诺：1520小时/周角色责任 ·设计挑战性的计算机问题来评估人工智能能力在科学软件使用中。·开发需要战略规划和实验以发现隐藏数据见解的问题。·测试并对最先进的 AI 模型进行改进，以实现目标难度。· 使用天体物理学和宇宙学专业知识，使用诸如天体科学等工具来创建问题。·独立且异步地工作以基于反的方法来完善问题的设计。必须有资格。· 在相关 STEM 领域 （MS,博士或相应）的研究生级培训。·通过研究或专业工作证明对科学软件库的熟练能力。· 写出问题设置和解决方案验证器的强大的 Python 技能。· 在 Linux/终端环境中使用远程计算沙盒进行工作的能力。· 每周可用1520小时。首选 · 多个域或工具中的经验。· 对基准或评估设计的熟悉性。· 经验在科学教学或考试/问题设计。· 有计算机可复制性和容器化环境的经验。申请过程 （需要20~30分钟才能完成）· 上传简历 · AI 采访根据你的简历 ·提交表格资源 & 支持 · 对面试进程和平台信息，请查看：· 任何帮助或支持，联系：PS:我们的团队每天审查应用程序。请完成你的 AI 采访和申请步骤以考虑这个机会。最初发布于喜马拉雅山。
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About the job
Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.
Position: Computational Astrophysics & Cosmology Expert
Type:Contract
Compensation:$70–$100/hour
Location:Remote
Commitment:15–20 hours/week
Role Responsibilities
- Design challenging computational problems to evaluate AI capabilities in scientific software usage.
- Develop problems requiring strategic planning and experimentation to uncover hidden data insights.
- Test and refine problems against state-of-the-art AI models to achieve target difficulty.
- Utilize Astrophysics & Cosmology expertise with tools like astropy for problem creation.
- Work independently and asynchronously to refine problem designs based on feedback.
Qualifications
Must-Have
- Graduate-level training in a relevant STEM field (MS, PhD, or equivalent).
- Proven proficiency with scientific software libraries through research or professional work.
- Strong Python skills for writing problem setups and solution validators.
- Ability to work in a Linux/terminal environment with remote compute sandboxes.
- Available for 15–20 hours/week.
Preferred
- Experience across multiple domains or tools.
- Familiarity with benchmark or evaluation design.
- Background in scientific teaching or exam/problem-set design.
- Experience with computational reproducibility and containerized environments.
Application Process (Takes 20–30 mins to complete)
- Upload resume
- AI interview based on your resume
- Submit form
Resources & Support
- For details about the interview process and platform information, please check:
- For any help or support, reach out to:
PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.
Originally posted on Himalayas