Pod lead - Electrical Engineering & Computer Science (Terminal Bench Science)
Pay listed on role
Verified partner opportunity
Payment: $300 per approved task
Computer Science (STEM) opportunity listed by Turing.
Computer Science (STEM) opportunity listed by Turing. Read the official requirements below before applying.
Official job description checked 2026-10-24:
Urgent hire: ASAP start, 4 week contract. Applications reviewed on a rolling basis, apply early.
Engagement details
Engagement type: Contractor, pay per task
Payment: $300 per approved task
Commitment: Minimum of 1 completed and approved task per day
No cap on tasks: Complete as many as you can until the pipeline is exhausted
Duration: 4 weeks, starting immediately
About Turing & Role
Turing is one of the world's leading AGI infrastructure companies, working with frontier AI labs to accelerate model development through high-quality training data, evaluations, and engineering talent. We are staffing a frontier AI data initiative building the infrastructure and training data used to develop and evaluate AI agents. You will be assigned to one of two tracks: connectors or tasks.
Responsibilities
Use AI coding agents throughout development, QA, and validation
Participate in onboarding, calibration, and quality reviews; deliver to the quality bar on agreed milestones
Project runs on two tracks:
Connecters track: Build Python backend applications that replicate existing SaaS tools (Slack, Linear, Jira, Notion, Gmail, wikis)
Test and validate connectors so each behaves faithfully like the system it emulates; extend existing connectors and build new ones within agreed timeframes.
Task track: Mine data and workflows to identify candidates for long-horizon task development, and author realistic tasks from them
Verify task quality, realism, and correctness through structured QA; write evaluation rubrics defining correct, partially correct, and deficient work
Requirements
3+ years of backend development experience with strong Python (FastAPI, Flask, or Django)
Git, Docker, and pipeline basics, with solid testing and code-quality practices
Experience building scalable backends, REST APIs, and microservices
Daily use of AI coding assistants (Claude Code, Codex, Cursor, GitHub Copilot, or similar)
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