Staff Software Engineer, Machine Learning

THE POSITION

Saildrone is seeking a Staff Machine Learning Engineer to join our team. Reporting directly to a Senior Member of the Machine Learning team, 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.

This role is required to be onsite in Alameda, CA in the Bay Area 3 days per week - this is our hybrid model. This is not a remote position.

THE TEAM

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.

THE RESPONSIBILITIES

• 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 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.