Machine Learning Engineer

Responsibilities

• build dynamic troubleshooting agents that understand networks
• solve unstructured production log data complexities
• optimize hardware utilization for data collection
• automate synthetic datasets creation
• architect data infrastructure for real-time network failures analysis
• design and scale automated pipelines transforming raw production logs into insights
• develop systems generating synthetic data for edge cases learning
• tackle unique network complexity problems
• optimize data collection and hardware utilization

• Bachelor's degree in STEM and 5+ years of relevant experience
• Master's degree in STEM and 3+ years of relevant experience
• PhD in STEM +0 years of relevant experience or equivalent related work experience
• 5+ years of experience in data engineering, machine learning engineering, or related roles
• Data Pipeline experience, designing and scaling data pipelines for unstructured or semi-structured data, including ingestion, cleansing, and auditing
• ML Infrastructure experience working with ML data workflows, including dataset creation, labeling, and evaluation
• Experience with Python and data processing frameworks (e.g., Spark, Beam, Ray)
• Experience with ML systems and tools, such as training pipelines and model evaluation frameworks
• Experience with human-in-the-loop ML systems, active learning, weak supervision or self-evolving agents (preferred)
• Exposure large language models, computer vision, or speech datasets (preferred)
• Experience building internal tools or platforms used by annotation or operations teams (preferred)

• Data Engineering
• Machine Learning Engineering
• Data Pipeline
• Data Processing
• Synthetic Data Creation
• Real-Time Analysis
• Network Troubleshooting
• Data Cleansing
• Model Evaluation
• Active Learning