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Xperteez TechnologyWorldwide
Opens the original posting on Himalayas.
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Performance Optimization
You’ll need: clear open source contributions and a profile to showcase them, such as GitHub (preferably with C++, Python, Java, Go, TypeScript, or Rust). If you don’t, you’ll be assessed through a HackerRank assessment instead.
Your tasks: Build reproducible Reinforcement Learning Environments that test an AI model’s ability to fix code, add features, refactor, and optimize performance - including a golden reference solution for each problem
Bonus: experts who complete the AI interview for this specific role move through the process significantly faster.
In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.
You'll create realistic, interesting and challenging coding tasks for AI to solve involving feature implementation and bug fixes, and implement deterministic verifiers that accept reasonable valid solutions and reject everything else.
Apply to the role, filling out the screening questions Complete AI interview (aprox. 20 minutes) Technical Assessment (if your GitHub profile doesn’t fully showcase the expertise required for this role)
Compensation is output-based; experts are paid per task that meets the project specifications. The time required to complete work may vary depending on the expert’s experience and workflow. Minimum submission requirements apply. Experts must submit a minimum of tasks per week.
We typically fill roles within 48 hours and are looking for experts ready to jump in right away. If selected, we expect you to start your first tasks within 24–48 hours of completing onboarding.
Originally posted on Himalayas
Opens the original posting on Himalayas.
Apply nowSource: Himalayas