About the company

A startup building the most comprehensive maritime intelligence platform in the world. Its flagship sensing system turns commercial and civilian vessels into a persistent, distributed sensing network pairing HD video, AI, radar, RF sensing, and AIS to deliver real-time maritime domain awareness at global scale. With more than 600 sensors deployed across 25+ countries and over 400,000 vessels identified outside of AIS, the company is redefining what ocean surveillance and safety can look like.

The team is mission-driven and fast-moving, building dual-use technology for defense agencies, coast guards, and commercial maritime operators.

The role

The company's edge platform is a vessel-mounted computing system running a dense software stack on NVIDIA Jetson hardware, in conditions that punish equipment: salt air, constant vibration, intermittent connectivity, and genuine operational pressure. This role owns the software running at the tip of the spear.

The person in this seat builds and maintains the embedded Linux platform that unifies HD cameras, radar, SDR, AIS receivers, GPS, and thermal sensors into a single AI-capable sensing system. The work sits at the intersection of hardware bringup, ROS2-based sensor middleware, edge inference pipelines, and the cloud connectivity layer that moves data from a vessel to analysts in near real time. It is complex, meaningful engineering, and it ships to sea.

Responsibilities

1. Own and evolve the embedded Linux platform running on edge devices
2. Integrate and maintain sensor interfaces across cameras, radar, SDR, AIS, GPS/GNSS, and thermal systems
3. Build modular middleware and services, including ROS2-based components, for reliable inter-process and inter-sensor communication
4. Develop and optimize edge AI inference pipelines for detection, segmentation, and classification under real compute and bandwidth limits
5. Design and improve edge-to-cloud data paths for latency, resilience, and efficient bandwidth use across constrained maritime networks
6. Manage OTA update workflows for fleet-deployed devices, including staged rollout validation and rollback strategy
7. Debug production issues from field telemetry, drive root-cause analysis, and ship durable fixes quickly
8. Partner with hardware, software, and operations teams to carry systems from lab bench to vessel deployment
9. Lead PTZ camera integration decisions — lens selection, sensor convergence and alignment, stabilization tuning — and translate those tradeoffs into measurable gains in edge ML performance across detection, classification, and tracking robustness

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Must-have qualifications

1. Strong embedded Linux engineering experience in production environments
2. Proven sensor integration work across multiple hardware interfaces and data streams
3. Strong software engineering fundamentals and robust development practices at scale: testing, observability, reliability, maintainability
4. Professional experience in Python, C++, and shell scripting
5. Experience with NVIDIA Jetson or a comparable edge compute platform
6. A track record in fast-moving, cross-functional teams with high ownership expectations
7. Solid networking fundamentals and practical remote debugging experience
8. Authorized to work in the U.S.

Additional qualifications

1. Bachelor's degree in computer science, robotics, electronics, electrical engineering, or a related field
2. 4+ years building software for robotics, electronics, and embedded systems
3. 4+ years of proficiency in Python, C++, and shell scripting; Rust is a plus
4. Solid Linux experience, particularly Ubuntu
5. Experience with robotics frameworks such as ROS or ROS2
6. Mobile and embedded platform development, specifically NVIDIA Jetson, CUDA, and Yocto
7. Familiarity with GPS, IMU, cameras, radar, and weather sensors, including driver development for them
8. Experience with industrial PTZ network cameras and video streaming technology
9. Hands-on sensor fusion across radar, EO/IR, and positioning data — Kalman/EKF, particle filters, or learned approaches
10. Developing and deploying AI/ML models for visual tasks: detection, segmentation, classification
11. OTA update systems and device fleet management at scale
12. Cloud infrastructure experience (AWS, Azure, GCP), including cloud-native ingestion services
13. Remote access management via SSH and VNC
14. A problem-solving, results-driven mindset with the flexibility to thrive in a dynamic environment

Nice to have

1. ROS/ROS2 middleware in robotics or autonomy systems
2. Industrial and network cameras with video streaming pipelines
3. Deep understanding of PTZ camera system design — lens and FOV tradeoffs, sensor convergence, mechanical and digital stabilization — and how those parameters affect ML accuracy, latency, and false positive/negative behavior in the field
4. Sensor fusion across EO/IR, radar, and positioning data
5. OTA and fleet management for distributed edge devices
6. Cloud ingestion pipelines on AWS, Azure, or GCP
7. Maritime, defense, autonomy, or other mission-critical deployment experience

Why this role

1. Hard technical problems at the intersection of embedded systems, AI, and real-world operations
2. Mission impact tied directly to maritime safety and security
3. Ownership and growth inside a fast-growing organization where high-quality work ships quickly