Software Engineer, Machine Learning

The Company

Saildrone is a maritime defense company and the global leader in autonomous unmanned surface vehicles (USVs). With more ocean miles and real-world operational experience than any USV manufacturer or operator, Saildrone maintains active, combat‑deployed systems supporting national security and force projection around the world, 24/7/365.

Saildrone’s manufacturing and R&D headquarters are located in Alameda, CA, with business development and sales operations in Washington, DC, and deployment hubs in Europe and the Middle East. By combining proven autonomous operations, edge computing, advanced sensing, renewable power, and the most advanced and robust unmanned surface technology on the planet, Saildrone is shaping how the Navy of the future operates.

Join a fast‑moving, mission‑driven team at the forefront of maritime security and autonomous innovation.

Saildrone is seeking a Staff Machine Learning Engineer to join our team. Reporting directly to the Director of Software Engineering, you will play a critical role in designing, deploying, and scaling machine learning systems that enable autonomy and real‑time intelligence across Saildrone’s global fleet. You will expand Saildrone’s model portfolio and ensure reliable, high‑performance inference on edge hardware in complex maritime environments.

We are looking for a technical leader who creates clarity from ambiguity, drives end‑to‑end execution, and takes ownership of production ML systems in mission‑critical environments.

The Machine Learning team is responsible for developing and deploying models that power perception, autonomy, and intelligence across Saildrone’s autonomous surface vehicles. We focus on building scalable, high‑performance ML systems that transform multimodal sensor data into actionable insights, enabling persistent maritime awareness in national security and defense environments.

• Production & Mission Impact:
  Design and deploy production‑grade ML models for real‑time perception to detect, classify, and track high‑value targets. Your work directly enables USVs to operate 24/7/365 in harsh, remote, and hostile maritime environments.
• Edge Architecture & Autonomy:
  Own the full ML lifecycle architecture, ensuring models run reliably on NVIDIA Jetson/AGX platforms. Drive the intelligence that advances autonomous decision‑making and behavior for a growing fleet of robotic systems.
• Multi‑Modal Sensor Fusion:
  Lead the integration of Saildrone’s unique sensor suite—including cameras, radar, lidar, hydrophones, and bathymetric sensors, to maintain situational awareness in complex, resource‑constrained environments.
• Dataset & Robustness Engineering:
  Drive the rapid expansion of proprietary maritime datasets and develop rigorous evaluation frameworks to ensure model performance remains stable across variable sea states and extreme weather.
• Strategic ML Ops:
  Architect and scale cloud‑based training pipelines and CI/CD workflows. You will resolve technical ambiguity to ensure the full stack from data ingestion to edge deployment is performant and maintainable.
• System‑Wide Optimization:
  Lead technical decisions for onboard compute efficiency using runtime libraries like Tensor
  
  RT, ensuring that rapid model iterations enhance rather than disrupt the overall software stack stability.
• Technical Leadership & Vision:
  Direct large‑scale ML projects from concept to completion. Mentor junior and senior engineers while shaping the technical roadmap for the global ML organization. Set engineering standards and influence architectural direction across multiple ML and autonomy teams.
• Cross‑Functional Integration:
  Serve as a technical leader across multiple engineering organizations—from Perception to Frontend—to ensure ML‑driven insights are actionable for mission pilots and critical to disrupting illegal maritime activity.

• BS or MS in Computer Science, Electrical Engineering, or a related technical field, as required for the design of complex autonomous systems.
• 10+ years of experience in Machine Learning or Software Engineering, performing work related to the full ML lifecycle, from cloud‑based…