Role Overview
We're looking for an Applied AI Engineer to join our MLOps team and take ownership of the infrastructure that keeps our machine learning models running reliably in production. This role is essential to maintaining the uptime and performance of our ML systems as usage scales. You'll work closely with data scientists, researchers, and software engineers to bridge the gap between experimentation and production—turning research artifacts into robust, monitored, and continuously improving services.
What You Will Do
Collaborate with experienced data scientists and software engineers to gain insights into building scalable and efficient data pipelines, model training, and deployment systems. Troubleshoot issues in the entire machine learning infrastructure, from Linux, Docker, and Kubernetes up to the highest levels of our ML stack.
Why It Might Be a Fit
3+ years of experience in MLOps or full stack Machine Learning. Good programming skills in a modern programming language (Python, Scientific Python Stack, Cuda). Understanding of the MLOps life cycle and experience with MLOps workflows.
Requirements
- 3+ years of experience in MLOps or full stack Machine Learning
- Good programming skills in a modern programming language (Python, Scientific Python Stack, Cuda)
- Understanding of the MLOps life cycle and experience with MLOps workflows
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