AI Cloud Infrastructure Engineer - Fury Team

The future of defense will be decided by those who field intelligent machines at scale. At Scout AI, we’re developing Fury, the first robotic foundation model for defense, to give U.S. forces overwhelming, adaptable, and autonomous power across every domain. Fury enables human operators to command fleets of robots through natural language, and empowers those machines to sense, decide, and act together as one. This mission will ask everything of us: urgency, precision, and relentless work.

The Role
We’re looking for an AI Infrastructure Engineer to build and scale the backbone of Fury’s model training and deployment ecosystem. You’ll design the data, compute, and orchestration infrastructure that enables our vision-language-action models to learn from massive real-world datasets and operate across edge and cloud environments. This role bridges systems engineering, distributed computing, and machine learning infrastructure. Your work will ensure our teams can iterate rapidly, train large models efficiently, and deploy them reliably on robotic platforms in the field.

We’re a startup. You’ll be moving fast, context-switching daily, and helping define the culture and process as we go. This is a rare opportunity to come in early and architect the future of defense.

Responsibilities

• Design and implement data pipelines for ingesting, transforming, and storing petabytes of multimodal data from Fury’s robotic and operator systems
• Develop internal tooling for dataset exploration, curation, versioning, and quality monitoring over time
• Build and maintain distributed training infrastructure (cloud and on-prem) for large-scale multimodal and foundation model training
• Implement job orchestration workflows for launching, tracking, and debugging large-scale model runs
• Identify and remediate bottlenecks in compute, memory, storage, and network performance to optimize throughput and cost efficiency
• Collaborate with AI, autonomy, and systems teams to ensure data and training infrastructure supports real-time and mission-critical use cases
• Maintain observability and reliability tooling for training and inference pipelines
• Stay current on best practices in MLOps, distributed training frameworks, and AI infrastructure at scale

Qualifications

• 3+ years of experience in ML infrastructure, MLOps, or large-scale data systems
• Proven experience with distributed training (PyTorch DDP, DeepSpeed, Ray, or similar) and workflow orchestration (Kubernetes, Airflow, or equivalent)
• Strong proficiency in Python and cloud-native infrastructure (AWS, GCP, or Azure)
• Deep understanding of data engineering (ETL pipelines, object storage, data versioning, metadata management)
• Familiarity with containerization and deployment (Docker, Kubernetes) and monitoring systems (Prometheus, Grafana)
• Experience optimizing GPU cluster utilization, scaling training jobs, and profiling model performance
• Bachelor’s degree or higher in Computer Science, Electrical Engineering, or related technical field
• Bonus: Experience with edge-deployed ML systems, federated training, or robotic data collection pipelines
• Must be a U.S. Person due to required access to U.S. export controlled information or facilities

Why Join Scout

• Work on the world’s most important frontier, ensuring U.S. and allied dominance in the age of intelligent machines
• Be a core part of a team building the first defense-specific robotic foundation model
• Collaborate with some of the top engineers in autonomy, AI, and national security
• See your work deployed on real systems
• Help define the future of intelligent defense systems
• Backed by Draper Associates, Booz Allen Ventures, and other top investors

Benefits

• Competitive compensation package including base salary and bonus.
• Meaningful equity
• Premium medical, dental, and vision plans with $0 paycheck contribution
• Competitive PTO and company holiday calendar
• Unlimited AI tokens
• Catered lunch daily and fully stocked kitchen
• EV charging
• Relocation assistance (depending on role eligibility)