Machine Learning Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer with a contract length of "unknown" and a pay rate of "unknown." Key skills include strong experience with scikit-learn, PySpark, and MLflow, along with proficiency in Python and model deployment practices.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
August 12, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
Unknown
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
Philadelphia, PA
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🧠 - Skills detailed
#Deployment #Python #Matplotlib #Model Deployment #Libraries #MLflow #Model Validation #Spark (Apache Spark) #GitHub #NumPy #Automation #Pandas #A/B Testing #Datasets #Cloud #Model Evaluation #Scala #Big Data #PySpark #ML (Machine Learning)
Role description
We're seeking a Machine Learning Engineer to develop and deploy scalable ML solutions using Python frameworks and big data technologies.
Responsibilities
• Build ML models using scikit-learn and process large datasets with PySpark
• Deploy models to production and monitor performance
• Implement feature engineering, model validation, and A/B testing
• Maintain ML infrastructure and ensure model reliability
Required Skills
• Strong experience with scikit-learn and PySpark
• Proficiency in Python and ML libraries (pandas, numpy, matplotlib, polars)
• Experience with MLflow for experiment tracking and model management
• Knowledge of GitHub Actions for ML pipeline automation
• Experience with model deployment and MLOps practices
• Understanding of statistical methods and model evaluation
• Familiarity with cloud platforms and containerization
We're seeking a Machine Learning Engineer to develop and deploy scalable ML solutions using Python frameworks and big data technologies.
Responsibilities
• Build ML models using scikit-learn and process large datasets with PySpark
• Deploy models to production and monitor performance
• Implement feature engineering, model validation, and A/B testing
• Maintain ML infrastructure and ensure model reliability
Required Skills
• Strong experience with scikit-learn and PySpark
• Proficiency in Python and ML libraries (pandas, numpy, matplotlib, polars)
• Experience with MLflow for experiment tracking and model management
• Knowledge of GitHub Actions for ML pipeline automation
• Experience with model deployment and MLOps practices
• Understanding of statistical methods and model evaluation
• Familiarity with cloud platforms and containerization
