Mechanical Engineer - Fully Remote | Up to $100/hr
Remote · Contratto
Sii il primo a candidarti
- Esperienza
- Qualsiasi
- Stipendio
- USD 70 – USD 100 / hour
- Aperture
- 1
- Pubblicato
- 3 settimane fa
- Modalità di lavoro
- Lavoro da casa
- Istruzione
- MS or PhD in a relevant STEM field
- Requisiti di ammissibilità
- Applicants with graduate-level STEM training, such as an MS, PhD, or equivalent research experience, are eligible to apply. The role is suited to professionals comfortable working remotely, independently, and on a part-time contract basis.
- Riprendere
- È necessario candidarsi
Descrizione del lavoro
About the role
Mercor works with top AI research teams to place highly skilled creative and technical professionals. The company is based in San Francisco and is backed by investors such as Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.
Role overview
This contract position focuses on STEM computational scientific software and evaluation design, with an emphasis on structural and mechanical engineering. The work is remote and expects a commitment of 15 to 20 hours per week. Compensation is between $70 and $100 per hour.
What you'll do
- Create advanced, graduate-level problem sets using domain-specific scientific software tools.
- Build tasks that require deliberate reasoning and the discovery of hidden details through experiments or queries.
- Tune assignments against leading AI systems and adjust them until the difficulty is at the intended level.
- Use scikit-fem or comparable finite element libraries for structural and mechanical engineering tasks.
- Operate independently in a Linux or terminal-based environment while using remote compute sandboxes.
- Work asynchronously with others to strengthen AI model performance and refine problem-solving approaches.
Required qualifications
Candidates should have graduate-level training in a STEM discipline, such as an MS, PhD, or equivalent research background. Strong Python skills are necessary, along with the ability to work autonomously, iterate on task design, and stay comfortable in Linux or terminal-based workflows.
Preferred background
Additional experience across multiple relevant tools or domains is valued. Familiarity with evaluation or benchmark design, experience creating scientific learning materials or exam-style questions, and exposure to reproducible workflows or containerized environments would be an advantage.
Application process
The application takes about 20 to 30 minutes and includes uploading a resume, completing an AI interview based on the resume, and submitting the form.
Resources and support
Interview and platform details are available through the provided documentation, and support is available by email.
Additional information
The review team checks applications daily, and candidates are expected to finish the AI interview and all application steps to be considered.