Freelance Machine Learning Engineer

Participation is project-based, not permanent employment.

What This Opportunity Involves

• 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 (days/weeks)
• 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

What We Look For

• 5+ years of hands‑on machine learning experience with proven business impact
• Portfolio of completed projects and publications showcasing real‑world problem‑solving
• Expert Python programming for data science (pandas, numpy, scipy, scikit‑learn, statsmodels)
• 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
• Knowledge of modern frameworks (TensorFlow, PyTorch, LangChain)
• Strong written English (C1+)

How It Works

Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid

Project time expectations

For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.

Compensation

On this project, contributors can earn up to $34 per hour equivalent , depending on their level and pace of contribution.

Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.