

Crossing Hurdles
Machine Learning Specialist | Remote
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Specialist with a contract length of approximately 20 hours per week, offering $80–$120/hour. Key skills include Python proficiency, ML libraries, and experience in model training. A PhD or strong ML engineering experience is required.
🌎 - Country
United Kingdom
💱 - Currency
$ USD
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💰 - Day rate
800
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🗓️ - Date
January 30, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
United Kingdom
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🧠 - Skills detailed
#ML (Machine Learning) #TensorFlow #Reinforcement Learning #Computer Science #Python #Libraries
Role description
Position: Machine Learning Engineer
Type: Hourly contract
Compensation: $80–$120/hour
Location: Remote (United States only)
Commitment: ~20 hours per week
Role Responsibilities
• Draft detailed natural-language plans and code implementations for machine learning tasks
• Convert novel ML problems into agent-executable tasks for reinforcement learning environments
• Identify failure modes in LLM-generated ML trajectories
• Apply golden patches to improve model behavior and training outcomes
• Contribute to benchmarking and improving model performance and training speed
Requirements
• Strong professional experience as a Machine Learning Engineer or a PhD in Computer Science
• Coursework or specialization in Machine Learning required
• Strong proficiency in Python and ML libraries such as TensorFlow, scikit-learn, XGBoost
• Experience with data preparation, model training, and evaluation workflows
• Bonus: Prior contributions to ML benchmarks
Application Process (Takes 20 Min)
• Upload resume
• System design session
• ML engineer screening form
Position: Machine Learning Engineer
Type: Hourly contract
Compensation: $80–$120/hour
Location: Remote (United States only)
Commitment: ~20 hours per week
Role Responsibilities
• Draft detailed natural-language plans and code implementations for machine learning tasks
• Convert novel ML problems into agent-executable tasks for reinforcement learning environments
• Identify failure modes in LLM-generated ML trajectories
• Apply golden patches to improve model behavior and training outcomes
• Contribute to benchmarking and improving model performance and training speed
Requirements
• Strong professional experience as a Machine Learning Engineer or a PhD in Computer Science
• Coursework or specialization in Machine Learning required
• Strong proficiency in Python and ML libraries such as TensorFlow, scikit-learn, XGBoost
• Experience with data preparation, model training, and evaluation workflows
• Bonus: Prior contributions to ML benchmarks
Application Process (Takes 20 Min)
• Upload resume
• System design session
• ML engineer screening form






