Astrophysics Researcher - Fully Remote | Upto $100/hr
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：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 ·设计具有挑战性的计算问题来测试人工智能系统使用真实的科学软件。·开发需要战略规划和多步的工作流的问题 AI 发现数据。·通过反复测试 AI 隐藏的状态模型来完善人工智能问题。· 使用天文学和相关工具进行宇宙学计算和数据分析。·独立工作以基于反的问题设计增强。·与团队协作，确保问题的难度是最佳的。必须有资格。· 在相关 STEM 领域 （MS,博士或相等研究经验）的毕业生级培训。·通过研究出版物或专业工作证明了科学软件库的能力。· 编写问题设置和解决方案验证器的 Python 技能强大。· 以远程计算机三盒子为基础的 Linux/终端环境中舒适。· 每周至少可使用1520小时。最好选择。· 经验跨多个领域或工具。· 对基准或评估设计的熟悉。· 科学教学或考试/问题组设计的背景。·计算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 面试根据你的简历 · 提交表格资源 & 支持 · 关于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 面试和申请步骤以考虑这个机会。原始地发布在喜马拉雅山脉。
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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: Astrophysics & Cosmology Expert
Type:Contract
Compensation:$70–$100/hour
Location:Remote
Commitment:15–20 hours/week
Role Responsibilities
- Design challenging computational problems to test AI systems using real scientific software.
- Develop problems requiring strategic planning and multi-step workflows for AI to uncover hidden data.
- Refine problems through iterative testing against state-of-the-art AI models.
- Utilize astropy and related tools for cosmological calculations and data analysis.
- Work independently to enhance problem designs based on feedback.
- Collaborate asynchronously with teams to ensure problem difficulty is optimal.
Qualifications
Must-Have
- Graduate-level training in a relevant STEM field (MS, PhD, or equivalent research experience).
- Proven proficiency with scientific software libraries through research publications or professional work.
- Strong Python skills for writing problem setups and solution validators.
- Comfortable in a Linux/terminal environment with remote compute sandboxes.
- Available for at least 15–20 hours per 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