Software Engineer - AI Research Annotation
Keystone Recruitment
Job Overview
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Job Description
Software Engineer - AI Research Annotation
Join leading AI labs by applying software engineering expertise to improve model reasoning. As a Software Engineer - AI Research Annotation, you will review real-world coding tasks and meticulously document problem-solving processes, contributing significantly to advanced AI development.
About the Project
This project supports the development of advanced AI coding capabilities through structured dataset creation. You will work with authentic software engineering scenarios drawn from open-source repositories to accurately capture realistic developer thought processes, which is crucial for training next-generation AI models.
Detailed Responsibilities
- Review AI-generated code trajectories on SWE-bench style tasks.
- Reconstruct step-by-step reasoning, thoroughly explaining solution approaches.
- Annotate decision points, debugging strategies, and technical choices made during development.
- Utilize full codebase context, including issues, tests, and pull requests, for accurate and comprehensive documentation.
- Ensure all annotations reflect authentic and high-quality software engineering practices.
Technical Requirements
- 2+ years of hands-on software development experience is essential.
- Strong proficiency in Python, JavaScript/TypeScript, or similar programming languages.
- Demonstrated experience debugging complex codebases and resolving intricate issues.
- Familiarity with Git workflows, including pull requests and issue tracking systems.
- Ability to articulate complex technical reasoning clearly and concisely in written form.
Project Structure
- Duration: The project is expected to last 1-2 months, with potential opportunities for extension based on performance and project needs.
- Start: Immediate onboarding for qualified candidates.
- Onboarding: The process includes a resume review followed by a 15-minute AI technical interview.
- Pace: Work is self-scheduled within a weekly commitment minimum of 15-25 hours, flexible up to 40 hours.
- Payment: Compensation is processed weekly via Stripe/Wise.
Ideal Background
- A Computer Science or Software Engineering degree (Bachelor's or higher) is preferred.
- Experience with open-source contributions or thorough repository analysis.
- A solid understanding of software development lifecycles and various testing methodologies.
- Comfort working extensively with technical documentation and specifications.
Success Profile
Top performers in the Software Engineer - AI Research Annotation role will receive rate increases and priority consideration for future AI research projects. Join an exclusive cohort of experienced engineers who are actively shaping the next generation of coding AI.
Key skills/competency
- AI Research
- Software Engineering
- Code Annotation
- Python
- JavaScript/TypeScript
- Debugging
- Git Workflows
- Technical Documentation
- Problem Solving
- Data Set Creation
How to Get Hired at Keystone Recruitment
- Research Keystone Recruitment's mission: Study their project goals, recent advancements in AI annotation, and how your skills align with their innovative work in AI.
- Tailor your resume effectively: Highlight your 2+ years of software development experience, proficiency in Python or JavaScript, and any experience with Git or open-source contributions to match the Software Engineer - AI Research Annotation role.
- Prepare for the AI technical interview: Brush up on debugging complex codebases, articulating technical reasoning, and be ready to discuss your experience with SWE-bench style tasks or similar code review scenarios relevant to Keystone Recruitment.
- Showcase open-source contributions: If you have experience with open-source repositories, analyzing codebases, or creating structured datasets, emphasize these experiences to demonstrate your direct relevance to the project.
- Highlight communication skills: The role requires clear written articulation of technical reasoning; prepare examples where you've documented complex technical choices or debugging strategies for Keystone Recruitment.
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