Quick Overview
Seniority
Mid Senior
Work mode
Hybrid
Location
Philadelphia, PA, United States
Posted
3 weeks ago
Machine LearningLLMPython
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.
Required Skills & Experience
Tech Breakdown
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.
Required Skills & Experience
- 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
- 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
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
- 70% Hands-On ML Model Building, Training, and Tuning
- 15% Collaboration & Explaining Models/Results to Peers
- 10% Technical Documentation & Data Workflows
- 5% Support & Optimization of Models in Production
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