Machine Learning Engineer job description

What is a Machine Learning Engineer?

A Machine Learning Engineer is a specialized software engineer who focuses on developing and implementing machine learning algorithms and systems. They possess a unique blend of software engineering skills and data science knowledge, enabling them to take theoretical models and research prototypes and turn them into production-ready, scalable applications. Their expertise lies in the entire ML lifecycle, from data collection and preprocessing to model training, evaluation, deployment, and monitoring.

What does a Machine Learning Engineer do?

Machine Learning Engineers are responsible for the end-to-end development of ML systems. Their core duties include designing data pipelines for efficient data ingestion and cleaning, implementing and training machine learning models using frameworks like TensorFlow or PyTorch, and deploying these models into production environments. They rigorously test model performance, optimize algorithms for scalability and latency, and continuously monitor and maintain deployed systems to ensure reliability and accuracy over time. Furthermore, they collaborate closely with data scientists to operationalize their models and with software engineers to integrate ML capabilities into larger applications.

Job Overview

We are seeking a highly skilled Machine Learning Engineer to design, develop, and deploy machine learning models and systems. The ideal candidate will have strong expertise in implementing ML algorithms, optimizing data pipelines, and creating scalable solutions that drive business value through data-driven insights and predictive analytics.

Machine Learning Engineer responsibilities include:

Must-Have Requirements

Preferred Qualifications

Bonus Skills