Machine Learning Engineer

Machine Learning Engineer

We’re hiring Machine Learning Engineers to join a cloud-native production engineering team focused on deploying, monitoring, and standardizing new ML modules at scale. This role is hands-on and engineering-driven — ideal for candidates who thrive building reliable ML systems in the cloud and ensuring business continuity once models are in production.

About the Role

You’ll work on high-impact projects implementing and maintaining machine learning modules in production environments. The goal: build scalable ML pipelines, ensure smooth monitoring, and minimize downtime. The ideal engineer has a strong software engineering foundation, solid Python skills, and experience deploying models to cloud environments (preferably GCP).

Responsibilities

• Develop and deploy ML models and pipelines into production.
• Standardize and monitor ML modules for performance and reliability.
• Integrate model APIs with existing cloud infrastructure.
• Automate workflows using CI/CD tools and version control (Git).
• Collaborate with DevOps and data teams to ensure robust, scalable deployment.
• Troubleshoot and optimize model performance and system uptime.

Required Skills

• 4+ years Software Engineering experience (Python-focused).
• 1+ year hands-on Machine Learning or Data Science in production.
• Strong experience with Python (must-have).
• GCP experience highly preferred (AWS or Azure okay if fast learner).
• Familiar with ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Working knowledge of SQL, Git, and CI/CD tools.
• Solid understanding of API integrations and containerization (Docker, etc.).

Nice to Have

• Experience with Java in addition to Python.
• Prior experience standardizing ML workflows or automating retraining pipelines.
• Familiarity with ML monitoring, drift detection, and model registry systems.

Why You’ll Love This

• 100% remote flexibility.
• Work on business-critical ML deployments with enterprise-scale visibility.
• Opportunity to help shape the standardization of ML in production across multiple projects.