Machine Learning Engineer

Motion Recruitment Partners LLC.
Philadelphia, PA, United States
5 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Big Data Data Cleansing Distributed Computing Environment Python (Programming Language) Machine Learning Feature Engineering Large Language Models Random Forest Model Validation Build Management Pyspark
+3 more
Xgboost Machine Learning Operations Software Coding

Job description

Empower our team as a Senior Machine Learning Engineer, where you’ll build and deploy reliable machine learning models that drive real results. We’re seeking someone with deep hands-on experience in traditional ML techniques and Python, your work will be central to our success.

This role is focused on practical model development, not just AI buzzwords. Join us to collaborate with a small, skilled team, sharpen your skills with large-scale data, and contribute directly to production solutions.

Requirements

  • Senior-level, hands-on experience as a Machine Learning Engineer (not AI/LLM-focused)
  • Advanced proficiency in Python, with coding skills comparable to a senior software engineer
  • Proven expertise building, training, evaluating, and deploying traditional ML models
  • Practical knowledge of Random Forest, XGBoost, and CatBoost (strongly preferred and currently in use)
  • Experience using PySpark or another distributed processing framework with machine learning workflows
  • Ability to clearly explain ML models and workflow, with real-world examples
  • Experience with data preparation, feature engineering, and model evaluation

Desired Skills & Experience

  • CatBoost production experience is a plus
  • Industry experience is open, telecom not required
  • Strong teamwork and communication skills
  • Previous experience with small, collaborative ML teams

What You Will Be Doing Tech Breakdown

  • 60% Machine Learning Model Development and Deployment (CatBoost, XGBoost, Random Forest)
  • 20% Big Data Processing and Integration (PySpark or equivalent)
  • 10% Data Preparation, Feature Engineering, Model Evaluation
  • 10% Collaborative Problem-Solving, Documentation, and Team Knowledge Sharing

Apply for this position

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