Machine Learning Engineer

Role overview

Build, scale, and maintain core machine learning infrastructure and production models for real-time personalization and predictive capabilities. You’ll translate research-grade algorithms into robust, low-latency services and work closely with product and data platform teams on end-to-end ML pipelines.

Responsibilities

• Design, build, and deploy production-grade ML models for classification, recommendation, and NLP use cases
• Develop and maintain scalable data pipelines and feature stores using Python, SQL, and PySpark for offline training and online serving
• Optimize inference performance (latency/throughput) for real-time APIs using Triton Inference Server, ONNX, or TensorRT
• Implement ML MLOps CI/CD pipelines using tools such as MLflow, Kubeflow, or AWS SageMaker for training, testing, and deployment
• Set up continuous monitoring and alerting for data drift, concept drift, and model performance degradation
• Write clean, modular, tested code in Python or Go; participate in code reviews and architecture discussions

Requirements

• 3+ years of professional software engineering or ML engineering experience, including deploying models to production
• Strong Python skills and deep familiarity with PyTorch, TensorFlow, or XGBoost
• Hands-on experience with AWS or GCP and containerization technologies like Docker and Kubernetes
• Solid understanding of software engineering best practices: version control, CI/CD, unit testing, system design
• BS or MS in Computer Science, Data Science, Mathematics, or a related quantitative field

Bonus / Nice-to-haves

• Experience with vector databases (Pinecone, Milvus)
• Experience with Triton
• Managing large-scale distributed training workloads