Staff Software Engineer, Machine Learning

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 TensorRT, 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.

THE QUALIFICATIONS

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 training to edge deployment.

Serve as a technical authority for edge AI systems operating in mission-critical environments.

Track record of leading cross-functional technical initiatives in autonomy, robotics, defense, or large-scale distributed systems

Demonstrated proficiency in Python, ML frameworks (PyTorch/TensorFlow), and runtime libraries (TensorRT) required to deploy performant computer vision and sensor fusion models.

Experience making system-level architecture and modeling decisions under high ambiguity, balancing performance, reliability, and compute constraints in real-world deployed ML systems

Track record of communicating technical vision, project roadmaps, and model performance metrics to peers, senior leadership, and cross-functional partners.

Ability to work effectively in remote or hybrid environments, including supporting a fleet that operates 24/7/365 in harsh and hostile maritime conditions.

Working knowledge of CI/CD best practices, Linux/Unix environments, and MLOps pipelines relevant to maintaining production-grade software at scale.

Demonstrated experience mentoring senior and junior engineers and leading end-to-end technical projects from initial concept to mission-ready fielding.

The base salary range for this position is shown below. Actual compensation may vary based on skills, experience, and qualifications. This range reflects the base salary for candidates in states requiring pay transparency disclosure.

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