Spear AI builds machine learning systems for the defense sector, running a heavily engineering-focused org (16 engineers across 22 open roles) with active work on real-time data pipelines, sensor integration, and policy frameworks for AI testing. The tech stack—PyTorch, TensorFlow, Kafka, Redpanda, AWS GovCloud—reflects both classical ML workflows and modern streaming infrastructure; the active shift to Rust and Redpanda while phasing out Protocol Buffers signals a move toward lower-latency, more efficient data handling in high-volume operational contexts.
Tech Stack 36 technologies
Core StackPyTorch GitHub Actions TensorFlow scikit-learn Python AWS JavaScript Java Go Apache Spark Kafka Apache Airflow Dagster PostgreSQL Apache Iceberg R Fairlearn SQL Azure GCP SIEM AWS GovCloud Parquet WebGPU MQTT Redpanda Google Workspace Kandji Vanta 1Password+6 more
ReplacingRedpanda Protocol Buffers
What Spear AI Is Building
◆Challenges
- Scaling internal infrastructure
- Replacing antique messaging systems
- Modernizing legacy codebase
- Implementing responsible ai frameworks
- Enterprise-scale data architecture
- High-volume low-latency data processing
- Secure data infrastructure
- Data governance compliance
- Real-time batch processing
- Solving hardest problems in national security
▲Active Projects
- Policy development for ai testing and evaluation across joint force
- Offline data pipelines
- Legacy codebase containerization
- Ic task force data infrastructure
- Real-time data pipelines with mqtt and redpanda
- Real-time data feeds
- Sonobuoy sensor development
- Ai project delivery
- Ml and analytics data pipelines
- Operational report automation
Hiring Activity
Steady20 roles · 9 in 30d
Department
Seniority
Company intelligence
