ML Infrastructure Engineer

The role

You will architect scalable cloud infrastructure for AI and data pipelines. You will develop and deploy training, validation, and distributed pipelines in the cloud.

What you will do

• Develop and deploy scalable AI training and validation pipelines in the cloud.
• Spin up distributed pipelines for data ingestion, pre-processing, training and evaluation.
• Deploy monitoring and CI/CD pipelines.
• Enable large-scale evaluation of AI models via cloud-based metrics.
• Enable large-scale evaluation of autonomy software and models via simulations in the cloud.
• Optimize performance, I/O and GPU utilization.
• Build tooling and dashboards for rapid experimentation, orchestration and visualization.
• Work with other teams to integrate cloud tooling into workflows.

Required qualifications

• Degree in computer science or related engineering disciplines (or equivalent experience).
• 4+ years of experience deploying high-performance ML pipelines in production.
• Proficient in Python and comfortable with C++/Go.
• Experience with ML frameworks like PyTorch.
• Experience with IO and data-loading workflows, including formats like Parquet, HDF5, TFRecord etc.
• Experience with deploying on cloud platforms like AWS, GCP or Azure.
• Experience with tooling like Docker, Kubernetes, and Airflow.
• Should be comfortable taking ownership of tasks with light supervision.
• Must have excellent problem-solving skills.
• Legally authorized to work in the United States.

About the company

Gritt is an intelligent system that combines robotics and AI to build infrastructure. The founding team includes experts in robotics and AI from Carnegie Mellon, Stanford, and MIT.