Pod Lead – Scientific Computing / Research Engineering Expert
Weekday AI
Pod Lead – Scientific Computing / Research Engineering Expert
Weekday AI
Proactive software engineer recruitment via referrals
Weekday AI · Bangladesh (Remote) · Contract · Posted 2026-10-19
Workplace: remote
Department: AI Training
This role is for one of our clients
We are seeking an experienced Pod Lead – Scientific Computing / Research Engineering Expert in Earth Sciences to support an advanced AI training and evaluation initiative. This role combines scientific domain expertise, research software engineering, computational problem-solving, and technical leadership to develop realistic, challenging, and objectively verifiable terminal-based scientific tasks.
The successful candidate will help design and validate scientific workflows involving climate science, atmospheric processes, geophysics, oceanography, geology, hydrology, remote sensing, and environmental modeling. As a Pod Lead, you will guide task development, maintain scientific and technical quality standards, and ensure that computational tasks accurately reflect real-world research challenges.
The ideal candidate will have advanced research experience in Earth Sciences or a related discipline, strong scientific programming skills, and practical experience working in Linux and terminal-based computational environments.
Job Details
Key Responsibilities
Required Qualifications
Preferred Qualifications
Account and Data Submission Requirements
Selection Process
Compensation and Engagement Terms
The role offers $30 per hour for approved project work. Candidates who submit the required JSON conversation-history file and are hired will receive an additional one-time payment of $50.
The assignment lasts 16 weeks and requires full-time availability, including at least 4 hours of daily overlap with PST. This is a contractor engagement and does not include medical benefits or paid leave.
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Proactive software engineer recruitment via referrals
Weekday.works helps companies hire software engineers through proactive outreach and peer-recommended candidates. Its platform automates tracking, pipeline management, and provides weekly updates, while it actively identifies engineers via peer networks, reaches out, shortlists, checks references, and schedules interviews. Differentiators include peer-backed vetting instead of traditional job boards, proactive outreach, and a passive-income reference feature that monetizes referrals. The goal is to reduce the time and effort of hiring by connecting companies with pre-vetted engineers who are open to new opportunities through a network-driven approach.
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