The Role
Develop real-time computer vision and perception systems for autonomous subsea vehicles, including obstacle detection, tracking, hazard avoidance, Visual SLAM, state estimation, and embedded GPU deployment. Build offline 3D reconstruction, semantic mapping, camera calibration, and sensor-fusion pipelines. Integrate cameras, lighting, and IMUs with hardware and embedded teams, then benchmark and field-test algorithms on subsea robotic platforms.
Summary Generated by Built In
About MOBY
MOBY Robotics builds autonomous deep-sea robotic systems that expand persistent access to the ocean. Our platform combines advanced computer vision, multi-agent coordination, and a scalable surface-to-seafloor architecture to deliver real-time ocean intelligence and enable responsible subsea operations.
About the Role
We are seeking a versatile, hands-on Computer Vision Engineer to lead the development of both real-time spatial perception and post-processing 3D reconstruction systems for MOBY’s subsea vehicles.
You will combine classical computer vision, modern vision models, and embedded deployment to build algorithms that run in real time on constrained hardware, working through non-uniform lighting, turbidity, marine life, and low-contrast seafloor.
Key Responsibilities
Real-Time Perception & Edge Navigation
• Build, optimize, and deploy real-time perception pipelines for obstacle detection, tracking, and hazard avoidance on embedded GPU hardware
• Develop robust Visual SLAM and state-estimation pipelines tuned for feature-sparse, visually degrading underwater environments
• Write highly efficient runtime code (C++/CUDA/TensorRT) maximizing frame rates while remaining compute- and memory-bounded on edge hardware
3D Spatial Reconstruction & Post-Processing
• Design and implement high-fidelity offline 3D reconstruction pipelines using multi-view stereo, 3D Gaussian Splatting, NeRFs, or structure-from-motion (SfM)
• Develop semantic tagging, object classification, and spatial map segmentation algorithms using state-of-the-art vision and vision-language models (VLMs/VLAs)
• Design camera calibration workflows (intrinsic, extrinsic, and multi-camera stereo) robust to refractive underwater optics and lens distortion
Hardware Prototyping & Field Integration
• Collaborate directly with hardware and embedded systems engineers to integrate cameras, lighting synchronization (PWM/hardware triggering), and IMU sensor fusion
• Rapidly prototype, benchmark, and field-test vision algorithms on real subsea crawler/ROV/AUV video feeds and datasets
Requirements
Required Qualifications
• Experience & Education: Master’s degree with 2+ years of relevant industry experience, or a Bachelor’s degree with 4+ years of relevant experience
• Core CV & Math: Deep understanding of classical computer vision (epipolar geometry, camera models, image restoration, feature extraction) and modern deep learning
• Underwater Visual Domain: Demonstrated experience handling subsea vision challenges (refractive optics, light attenuation, backscatter, non-uniform illumination, and low-contrast marine terrain)
• SLAM & State Estimation: Hands-on experience with Visual-Inertial SLAM (e.g., ORB-SLAM3, OKVIS, VINS-Mono) or sensor fusion algorithms
• Embedded Deployment & Compute Optimization: Proven capability deploying models to edge platforms (e.g., NVIDIA Jetson / CUDA / TensorRT / ONNX Runtime) with strict latency and memory budgets
• Practical Prototyping Skills: Strong proficiency in Python and C++, with hands-on experience using OpenCV, PyTorch/LibTorch, ROS 2, and point cloud libraries (PCL/Open3D)
Preferred Qualifications
• Experience with 3D Gaussian Splatting, Neural Radiance Fields (NeRFs), or dense photogrammetry pipelines
• Familiarity with hardware-triggered multi-camera synchronization and subsea lighting controls
• Exposure to Vision-Language Models (VLMs) or vision-guided manipulation and grasping
• Experience processing multi-modal sensor streams (DVL, IMU, sonar, monocular/stereo RGB)
Benefits
What We Offer
• Competitive pay
• Direct ownership of a critical, foundational platform in deep-sea robotics
• Small, highly technical team with broad systems exposure
• Rapid iteration between algorithm design, field testing, and real subsea deployment
• Opportunity to shape next-generation ocean intelligence infrastructure
Skills Required
- Master's degree with at least 2 years of relevant industry experience, or bachelor's degree with at least 4 years of relevant experience
- Strong understanding of classical computer vision, including epipolar geometry, camera models, image restoration, and feature extraction
- Experience with modern deep learning
- Demonstrated experience addressing underwater vision challenges such as refractive optics, light attenuation, backscatter, non-uniform illumination, and low-contrast terrain
- Hands-on experience with Visual-Inertial SLAM or sensor-fusion algorithms
- Experience deploying models to edge platforms with strict latency and memory constraints
- Strong proficiency in Python and C++
- Hands-on experience with OpenCV, PyTorch or LibTorch, ROS 2, and point-cloud libraries such as PCL or Open3D
- Experience with 3D Gaussian Splatting, Neural Radiance Fields, or dense photogrammetry pipelines
- Familiarity with hardware-triggered multi-camera synchronization and subsea lighting controls
- Exposure to Vision-Language Models or vision-guided manipulation and grasping
- Experience processing DVL, IMU, sonar, and monocular or stereo RGB sensor streams
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The Company
What We Do
MOBY Robotics develops autonomous deep-sea robotic systems for persistent ocean access and responsible critical-mineral operations. Its platform combines AI-powered computer vision, coordinated swarm robotics, and a scalable surface-to-seafloor architecture. The company’s systems include autonomous seafloor crawlers, underwater vehicles, and a mineral production platform designed to identify and collect nickel, copper, cobalt, and manganese while minimizing environmental disturbance in challenging deep-ocean environments.
