Machine Learning Engineer

About The Role

The role owns the end-to-end lifecycle of production machine learning systems, from designing scalable training pipelines to deploying low-latency models that serve core product features.

The engineering team works closely with applied researchers and backend developers to build robust infrastructure where model performance, cost, and reliability are strictly balanced.

Key Responsibilities

• Architect and implement distributed machine learning pipelines using Python, PyTorch, and Apache Spark for large-scale data processing
• Deploy, monitor, and scale models in production using cloud infrastructure such as AWS, Docker, and Kubernetes
• Optimize model inference latency, throughput, and memory footprint through quantization, pruning, and ONNX runtime
• Build automated monitoring frameworks to detect feature drift, data quality issues, and performance degradation in real-time
• Collaborate with data engineering teams to define feature stores and ensure consistency between training and inference data
• Write clean, highly maintainable code, conduct thorough peer code reviews, and contribute to system architecture documentation
  
  

What We Are Looking For

• 3-6 years of professional software engineering experience, with at least 3 years focused specifically on machine learning engineering
• Strong proficiency in Python and deep hands-on experience with production-grade ML frameworks like PyTorch or TensorFlow
• Demonstrated experience deploying and maintaining containerized ML models in cloud environments such as AWS, GCP, or Azure
• Solid understanding of software engineering best practices, including CI/CD pipelines, automated testing, and infrastructure-as-code
• BS or MS in Computer Science, Machine Learning, Statistics, or a related technical field
• Bonus: Experience with LLM fine-tuning, RAG architectures, or contributing to major open-source ML projects