

Crossing Hurdles
Machine Learning Engineer | Remote
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is a Machine Learning Engineer contract position offering $30 - $100/hour for 10-40 hrs/week, focusing on TensorFlow and Scikit-Learn. Key requirements include expertise in model development, data preprocessing, and production-ready systems. Remote work is expected.
🌎 - Country
United Kingdom
💱 - Currency
$ USD
-
💰 - Day rate
800
-
🗓️ - Date
May 27, 2026
🕒 - Duration
Unknown
-
🏝️ - Location
Remote
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
United Kingdom
-
🧠 - Skills detailed
#TensorFlow #Data Cleaning #Deployment #Business Analysis #Data Science #Model Evaluation #Scala #"ETL (Extract #Transform #Load)" #Data Engineering #ML (Machine Learning)
Role description
Position: Machine Learning Engineer
Type: Contract
Compensation: $30 - $100/hour
Location: Remote
Commitment: 10-40 hrs/week
Role Responsibilities
• Develop, train, and deploy machine learning models using TensorFlow and Scikit-Learn to solve real-world business problems.
• Conduct comprehensive data preprocessing, including data cleaning, transformation, and featureengineering to ensure high-quality inputs for model development.
• Collaborate with data scientists, data engineers, and business analysts to understand requirements and translate them into scalable machine learning solutions.
• Continuously monitor, evaluate, and refine model performance to ensure accuracy, efficiency, and reliability in production environments.
• Document model development processes and communicate technical concepts and results with both technical and non-technical team members.
• Stay updated on the latest research and advancements in machine learning and apply innovative techniques to enhance project outcomes.
Requirements:
• Have expert-level proficiency in TensorFlow and Scikit-Learn for end-to-end model development and deployment.
• Possess a strong background in data preprocessing, including handling missing values, outlier detection, and feature selection.
• Have demonstrated experience in designing and implementing production-ready machine learning systems.
• Have a solid understanding of algorithm selection, model evaluation metrics, and performance tuning.
• Be able to work independently and remotely, delivering results in a fast-paced, deadline-driven environment.
Application Process:
• Easy Apply on LinkedIn
• Check email for next steps
• Participate in resume evaluation & interview stage
Position: Machine Learning Engineer
Type: Contract
Compensation: $30 - $100/hour
Location: Remote
Commitment: 10-40 hrs/week
Role Responsibilities
• Develop, train, and deploy machine learning models using TensorFlow and Scikit-Learn to solve real-world business problems.
• Conduct comprehensive data preprocessing, including data cleaning, transformation, and featureengineering to ensure high-quality inputs for model development.
• Collaborate with data scientists, data engineers, and business analysts to understand requirements and translate them into scalable machine learning solutions.
• Continuously monitor, evaluate, and refine model performance to ensure accuracy, efficiency, and reliability in production environments.
• Document model development processes and communicate technical concepts and results with both technical and non-technical team members.
• Stay updated on the latest research and advancements in machine learning and apply innovative techniques to enhance project outcomes.
Requirements:
• Have expert-level proficiency in TensorFlow and Scikit-Learn for end-to-end model development and deployment.
• Possess a strong background in data preprocessing, including handling missing values, outlier detection, and feature selection.
• Have demonstrated experience in designing and implementing production-ready machine learning systems.
• Have a solid understanding of algorithm selection, model evaluation metrics, and performance tuning.
• Be able to work independently and remotely, delivering results in a fast-paced, deadline-driven environment.
Application Process:
• Easy Apply on LinkedIn
• Check email for next steps
• Participate in resume evaluation & interview stage





