Machine Learning Engineer

Role Overview

Own the end-to-end lifecycle of production machine learning systems—from scalable training pipelines to deploying low-latency models that support core product features.

Responsibilities

• Architect and implement distributed machine learning pipelines using Python, PyTorch, and Apache Spark.
• Deploy, monitor, and scale production models using cloud infrastructure such as AWS, Docker, and Kubernetes.
• Optimize inference latency, throughput, and memory via quantization, pruning, and ONNX Runtime.
• Build automated monitoring to detect feature drift, data quality issues, and performance degradation in real time.
• Collaborate with data engineering teams to define feature stores and ensure training/inference data consistency.
• Write maintainable code, perform thorough peer code reviews, and contribute to system architecture documentation.

Requirements

• 3–6 years of professional software engineering; at least 3 years focused on machine learning engineering.
• Strong Python skills and deep hands-on experience with production-grade ML frameworks (PyTorch or TensorFlow).
• Experience deploying and maintaining containerized ML models in cloud environments (AWS, GCP, or Azure).
• Solid software engineering practices: CI/CD, automated testing, and Infrastructure-as-Code.
• BS or MS in Computer Science, Machine Learning, Statistics, or related technical field.

Nice to Have

• Experience with LLM fine-tuning and/or RAG architectures.
• Experience contributing to major open-source ML projects.