RF Circuit Design Expert - PhD
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：计算电气工程&RF/循环设计专家类型：合同补偿：$70$85/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 AI 能力在解决科学任务中。·开发需要使用专门的科学软件如 scikit-rf 和 ngspice 等的问题。· 测试和精炼与最先进的 AI 模型进行问题，以实现目标难度。· 与研究团队合作提高人工智能模型性能和培训数据质量。·独立和异步工作，以满足截止日期并精细设计问题。必须有资格。· 在相关 STEM 领域 （MS,博士或同等研究经验）学士级培训。· 具有科学软件库如 scikit-rf 或 ngspice 的证明能力。· 编写问题设置和解决方案验证器的能力强大。· 使用远程计算沙盒在 Linux/终端环境中工作的能力。· 每周可至少1520小时使用。最好选择 Python. · 经验在多个列表领域或工具。· 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 · 提交形式资源 & 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。原始地发布于喜马拉雅山脉
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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: Computational Electrical Engineering & RF/Circuit Design Expert
Type:Contract
Compensation:$70–$85/hour
Location:Remote
Commitment:15–20 hours/week
Role Responsibilities
- Design challenging computational problems to evaluate AI capabilities in solving scientific tasks.
- Develop problems requiring the use of specialized scientific software like scikit-rf and ngspice.
- Test and refine problems against state-of-the-art AI models to achieve target difficulty.
- Collaborate with research teams to improve AI model performance and training data quality.
- Work independently and asynchronously to meet deadlines and refine problem designs.
Qualifications
Must-Have
- Graduate-level training in a relevant STEM field (MS, PhD, or equivalent research experience).
- Proven proficiency with scientific software libraries like scikit-rf or ngspice.
- Strong Python skills for writing problem setups and solution validators.
- Ability to work in a Linux/terminal environment with remote compute sandboxes.
- Available for at least 15–20 hours per week.
Preferred
- Experience across multiple listed 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