Computational Biology Expert | $80-$100/hr Remote
Overview
A remote contractor role for a Computational Biology Expert. Help train next generation AI systems by providing domain-specific input that guides learning, reasoning, and performance. Bring deep knowledge of computational biology and experience with AI coding agents to real-world, complex workflows.
What You'll Do5
- 1Work on tasks that test and improve AI coding agents in computational biology contexts.
- 2Demonstrate practical use cases of agents like Codex, Claude Science, Claude Code, and Claude Cowork in advanced biology scenarios.
- 3Document comprehensive workflows and outcomes from agent-assisted problem solving, focusing on sequence generation and technical challenges.
- 4Provide precise written and spoken feedback on model performance, usability, and integration potential in STEM settings.
- 5Share insights from navigating large codebases, debugging intricate issues, and architecting biology-focused solutions.
Requirements5
- 1Solid background in computational biology with track record of delivering complex AI-enabled projects.
- 2Hands-on experience with AI coding agents, especially Codex and Claude family tools, in authentic workflows.
- 3Experience working with sizable codebases and implementing end-to-end features in computational biology contexts.
- 4Strong written and verbal communication, capable of detailing problem-solving steps and technical results.
- 5Active engagement in applying AI tools to real-world problems and providing constructive feedback for system improvement.
Who Should Apply
Ideal candidates are seasoned computational biologists who actively use AI coding agents to solve real scientific problems. You should be comfortable documenting workflows, sharing detailed feedback, and collaborating with a project team to refine tools for complex biology tasks.
Salary Insight
Hourly rate ranges from $80 to $100.
Location
Required Skills
Application Tip
Prepare a brief case study showing how you used an AI coding agent to address a specific computational biology challenge, including the problem, approach, outcomes, and lessons learned.
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