Machine Learning Engineer

About The Role

The role owns the end-to-end lifecycle of machine learning systems, from experimental research and data pipelining to scalable production deployments serving real-time traffic.

The engineering team works at the intersection of applied research and core backend development, focusing on low-latency inference, model robustness, and efficient MLOps practices.

Key Responsibilities

• Design and train production machine learning models using PyTorch or TensorFlow, focusing on high accuracy and low latency
• Build robust data ingestion and feature engineering pipelines in Python and SQL to support model training and inference
• Deploy and manage ML models in production environments using Docker, Kubernetes, and cloud platforms like AWS or GCP
• Implement automated monitoring tools to track model drift, latency metrics, and infrastructure performance
• Collaborate with software engineering and product teams to integrate models into customer-facing applications
• Conduct code reviews, write comprehensive tests, and maintain architectural documentation for ML systems
  
  

What We Are Looking For

• 3 to 6 years of professional software engineering experience, with at least 3 years focused on machine learning engineering
• Strong proficiency in Python and hands-on experience with modern deep learning frameworks such as PyTorch
• Demonstrated experience deploying and maintaining machine learning models in production environments using cloud infrastructure
• Solid foundation in software engineering best practices, including CI/CD pipelines, containerization, and version control
• Bachelor's or Master's degree in Computer Science, Statistics, Data Science, or a related technical field
• Bonus: Experience with LLM orchestration frameworks, vector databases, or real-time streaming data architectures like Kafka