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Salary: USD 144,500 - 322,742 / annual
Saildrone is a maritime defense company and global leader in autonomous unmanned surface vehicles (USVs). The company operates combat-deployed systems supporting national security and force projection worldwide, with manufacturing and R&D headquarters in Alameda, CA, and deployment hubs in Europe and the Middle East.
As a Perception Machine Learning Engineer, you will develop and deploy ML-based perception capabilities for autonomous vehicles operating in challenging, poorly observed maritime environments. You will work across the full lifecycle: model development, data engineering, evaluation, and production deployment.
Key responsibilities include:
- Developing machine-learning-based perception systems for autonomous vehicles
- Training, evaluating, and improving computer vision models using real-world sensor data
- Creating data and experimentation workflows to identify failure modes and drive performance improvements
- Building perception capabilities for detection, classification, segmentation, depth estimation, and scene understanding
- Analyzing vehicle data and model failures to identify weaknesses
- Integrating models into production perception systems
- Transitioning new ML techniques from experimentation into deployed systems
This is a hands-on role that bridges research and production, requiring someone who can move models from prototype to real-world deployment in defense-critical applications.
QUALIFICATIONS (Required):
- Strong Python programming skills
- Hands-on experience developing computer vision or machine learning systems
- Strong experience with PyTorch or comparable ML framework
- Strong understanding of computer vision fundamentals and real-world visual data
- Experience evaluating models using quantitative metrics and experimental analysis
- Experience taking ML or computer vision systems into production
NICE TO HAVE:
- C++ experience for production perception systems
- Robotics, autonomous vehicle, aerospace, or defense experience
- Edge or embedded ML deployment
- Sensor fusion, 3D perception, or multimodal perception
- ONNX, TensorRT, CUDA, or other inference optimization experience
