Systems Biology Specialist - Fully Remote | Upto $85/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：计算药物动力学和系统生物学专家类型：合同补偿：$70 $85/小时 地点：远程承诺：1520小时/周角色责任 ·设计 AI 使用真实的科学软件解决具有挑战性的计算机问题。·开发需要战略规划和执行多步骤的工作流程的问题。·测试并对最先进的 AI 模型进行改进来实现目标难度。· 使用 libRoadRunner,Tellurium 或 SBML 为区间 PK/PD 建模和系统生物学模拟的工具。·独立工作以基于反来完善问题设计。·协作异步并保持灵活时间。必须有资格。·在相关 STEM 领域 （MS,博士学位或相等研究经验）的研究生级培训。·至少有一本列出的科学软件库具有证明能力。·强大的技能。· 在远程计算沙盒的 Linux /终端环境中工作的能力。· 每周可使用至少15~20小时。首选 · 多个列出的域或工具中的经验。·熟悉基准或评估设计。· 科学教学或考试/问题组设计的背景。· 有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 · 对面试进程和平台信息详细，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 面试和申请步骤将被考虑为此机会。原始地发布在喜马拉雅山。
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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 Pharmacokinetics & Systems Biology Expert
Type:Contract
Compensation:$70–$85/hour
Location:Remote
Commitment:15–20 hours/week
Role Responsibilities
- Design challenging computational problems for AI to solve using real scientific software.
- Develop problems requiring strategic planning and execution of multi-step workflows.
- Test and refine problems against state-of-the-art AI models to achieve target difficulty.
- Utilize libRoadRunner, Tellurium, or SBML-based tools for compartmental PK/PD modeling and systems biology simulations.
- Work independently to refine problem designs based on feedback.
- Collaborate asynchronously and maintain flexible hours.
Qualifications
Must-Have
- Graduate-level training in a relevant STEM field (MS, PhD, or equivalent research experience).
- Proven proficiency with at least one listed scientific software library.
- Strong Python skills.
- Ability to work in a Linux/terminal environment with remote compute sandboxes.
- Available for at least 15–20 hours/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