Machine Learning Engineer

Role Overview

Build, scale, and maintain core machine learning models and pipelines that power real-time decision-making systems. Translate applied research into high-throughput production services while meeting strict performance, latency, and reliability requirements.

Responsibilities

• Design and deploy production-grade ML models for real-time inference, including:
  • Recommendation systems
  • Classification
  • Predictive modeling
• Build and optimize scalable data preprocessing and feature engineering pipelines using Apache Spark, Ray, or PySpark
• Create and maintain automated CI/CD pipelines for ML training, evaluation, and deployment using tools such as Kubeflow, MLflow, or Argo Workflows
• Implement real-time monitoring and alerting for:
  • Model drift / concept drift
  • System latency
• Optimize inference performance using quantization, pruning, and GPU acceleration with TensorRT or ONNX
• Collaborate with platform engineers to scale vector databases and feature stores for low-latency retrieval

Requirements

• 3–6 years professional experience as an ML Engineer (or Software Engineer) working with production-grade ML systems
• Strong proficiency in Python and hands-on experience with core ML frameworks such as PyTorch, TensorFlow, or XGBoost
• Experience deploying models to cloud environments (AWS, GCP, or Azure) using Docker and Kubernetes
• Solid software engineering fundamentals (e.g., version control, unit testing, design patterns)
• BS/MS in Computer Science, Data Science, Mathematics, or related field

Bonus

• Experience with Triton Inference Server (including Triton CLI) or building custom Triton backends for high-throughput serving