Role overview
We're seeking a skilled Machine Learning Engineer to build and deploy production ML systems for the next-generation data management and artificial intelligence platform for maritime domain awareness.
Spear AI is a growing defense contracting company dedicated to delivering cutting-edge solutions that support our nation's security. As we expand, we're building a culture where innovation meets mission-critical work. We operate with a flat organizational structure that empowers every team member to make an impact, collaborate directly with leadership, and contribute to projects that matter. Whether you're joining our Hardware, Software, or Services division, you'll work alongside talented professionals who are committed to excellence and advancing the capabilities that keep our nation safe and secure.
Spear AI builds sonobuoy sensors that are deployed into the water and collect edge data. We also work with the U.S. Navy to collect and process their SONAR data. You'll have an opportunity to work on real-world projects that directly impact warfighter capabilities and mission success.
What you'll work on
- We're a small team wearing many hats, and you'd have a wide variety of responsibilities that include:
- Design, train, and optimize machine learning models using PyTorch
- Deploy models to production environments in the cloud and at the edge
- Build and maintain ML pipelines for training, evaluation, and inference
- Integrate machine learning models into real-time and batch processing systems
- Optimize model performance for accuracy, latency, and resource constraints
- Implement model monitoring, versioning, and deployment strategies
- Work with signal processing data and time-series analysis
- Improve local development and CI/CD for ML workflows using modern tooling and GitHub Actions
What we're looking for
- Experience with reinforcement learning algorithms and applications
- Digital signal processing experience
- Background in time-series analysis or sensor data processing
- Experience with edge deployment and model optimization for resource-constrained environments
- Familiarity with distributed training across multiple GPUs/nodes
- Experience with model compression techniques (quantization, pruning, distillation)
- Contributions to open-source ML projects or research publications
- Experience in defense, aerospace, or other regulated industries