Tech Stack

Description

You will build and scale the infrastructure for training and deploying Fury, Scout AI's defense robotic foundation model. You'll design data pipelines, distributed training systems, and orchestration workflows to enable rapid iteration and reliable edge deployment.

Requirements

• 3+ years in ML infrastructure, MLOps, or large-scale data systems
• Experience with distributed training (PyTorch DDP, DeepSpeed, Ray) and orchestration (Kubernetes, Airflow)
• Strong proficiency in Python and cloud-native infrastructure (AWS, GCP, Azure)
• Deep understanding of data engineering (ETL, object storage, versioning)
• Bachelor's degree in CS, EE, or related field; U.S. Person required

Responsibilities

• Design and implement data pipelines for petabyte-scale multimodal data
• Develop internal tooling for dataset curation, versioning, and quality monitoring
• Build and maintain distributed training infrastructure for foundation models
• Implement job orchestration workflows for large-scale model runs
• Optimize compute, memory, and network performance for cost efficiency