Computer Vision Engineer

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 hardwareDevelop robust Visual SLAM and state-estimation pipelines tuned for feature-sparse, visually degrading underwater environmentsWrite highly efficient runtime code (C++/CUDA/TensorRT) maximizing frame rates while remaining compute- and memory-bounded on edge hardware3D 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 distortionHardware Prototyping & Field Integration

Collaborate directly with hardware and embedded systems engineers to integrate cameras, lighting synchronization (PWM/hardware triggering), and IMU sensor fusionRapidly prototype, benchmark, and field-test vision algorithms on real subsea crawler/ROV/AUV video feeds and datasetsRequirements

Required Qualifications

Experience & Education: Master’s degree with 2+ years of relevant industry experience, or a Bachelor’s degree with 4+ years of relevant experienceCore CV & Math: Deep understanding of classical computer vision (epipolar geometry, camera models, image restoration, feature extraction) and modern deep learningUnderwater 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 algorithmsEmbedded Deployment & Compute Optimization: Proven capability deploying models to edge platforms (e.g., NVIDIA Jetson / CUDA / TensorRT / ONNX Runtime) with strict latency and memory budgetsPractical 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 pipelinesFamiliarity with hardware-triggered multi-camera synchronization and subsea lighting controlsExposure to Vision-Language Models (VLMs) or vision-guided manipulation and graspingExperience processing multi-modal sensor streams (DVL, IMU, sonar, monocular/stereo RGB)Benefits

What We Offer

Competitive payDirect ownership of a critical, foundational platform in deep-sea roboticsSmall, highly technical team with broad systems exposureRapid iteration between algorithm design, field testing, and real subsea deploymentOpportunity to shape next-generation ocean intelligence infrastructure

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)

• Small, highly technical team with broad systems exposure

• Rapid iteration between algorithm design, field testing, and real subsea deployment

Benefits

• Competitive pay

• Direct ownership of a critical, foundational platform in deep-sea robotics

• Opportunity to shape next-generation ocean intelligence infrastructure

Responsibilities

• 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

• 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