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About the Role

Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.

Responsibilities

• Design original computational STEM problems that simulate real scientific workflows
• Create problems that require Python programming to solve
• Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes
• Develop problems requiring non-trivial reasoning chains and creative problem-solving approaches
• Verify solutions using Python with standard libraries (Numpy, Pandas, Scipy, scikit-learn)
• Document problem statements clearly and provide verified correct answers

Requirements

• ML specialists with experience in Python
• Open to part-time, non-permanent projects
• 5+ years of hands-on machine learning experience with proven business impact
• Portfolio of completed projects and publications showcasing real-world problem-solving
• Expert statistical analysis and machine learning - deep understanding of algorithms, methods, and their practical applications
• Expert with SQL and database operations for data manipulation and analysis
• Experience with GenAI technologies (LLMs, RAG, prompt engineering, vector databases)
• Understanding of MLOps practices and model deployment workflows
• Strong written English (C1+)
• Submit CV in English and indicate English proficiency level

Skills

• Python programming for data science (pandas, numpy, scipy, scikit-learn, statsmodels)
• GenAI technologies (LLMs, RAG, prompt engineering, vector databases)
• MLOps practices
• Model deployment workflows
• TensorFlow
• PyTorch
• LangChain

Location

• Remote

Work Type

• Project-based
• Part-time
• Non-permanent

Experience Level

• 5+ years of hands-on machine learning experience

Salary/Compensations

• Up to $58 per hour equivalent

About the Company

• Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.