Computational Biologist - Fully Remote | Upto $100/hr
Remote · Contract
Be the first to apply
- Experience
- Any
- Salary
- USD 70 – USD 100 / hour
- Openings
- 1
- Posted
- 3 weeks ago
- Work mode
- Work from home
- Education
- MS/PhD or equivalent research experience
- Eligibility
- Candidates with graduate-level STEM training and relevant scientific software experience who can work 15 to 20 hours per week on a remote contract basis.
- Resume
- Required to apply
Job description
About the Role
Mercor partners with top-tier creative and technical professionals to support leading AI research labs. The company is based in San Francisco and is backed by Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.
Position
This opening is for a STEM-focused computational scientific software and evaluation design role centered on pharmacokinetics and systems biology.
Compensation and Commitment
The pay range for this contract role is $70 to $100 per hour. The expected workload is 15 to 20 hours each week, and the work is fully remote.
Responsibilities
- Create difficult computational tasks using scientific software libraries and tools such as libRoadRunner, Tellurium, and SBML-based systems.
- Build problem sets that assess whether AI systems can carry out multi-step scientific workflows and apply strong reasoning.
- Adjust and fine-tune tasks against current AI models so the challenge level matches the intended difficulty.
- Partner with AI research teams to raise the quality of model outputs and training data.
- Operate independently in an asynchronous setup and deliver work within deadlines.
Qualifications
Applicants should have graduate-level preparation in a relevant STEM field, such as an MS, PhD, or comparable research background. They must also have hands-on experience with at least one of the listed scientific software libraries, demonstrated through publications, open-source work, or professional experience. Strong Python skills are required for creating problem setups, oracle functions, and validation logic. Candidates should be able to iterate on task design based on calibration feedback, work well on their own, and feel comfortable using Linux, terminal tools, and remote compute sandboxes.
Preferred Experience
- Exposure to more than one of the relevant scientific domains or software tools.
- Prior experience designing benchmarks or evaluation tasks.
- Experience in scientific teaching, exam creation, or problem-set development.
- Background in reproducible research and containerized workflows.
Application Process
The application takes about 20 to 30 minutes and includes three steps: uploading a resume, completing an AI interview based on the resume, and submitting the form.
Resources and Support
For information about the interview flow and platform details, candidates are directed to the provided documentation. Support requests can be sent to the listed support email address.
Additional Information
The hiring team reviews applications daily. Candidates must complete both the AI interview and the rest of the application to be considered.