Senior Computer Vision Engineer

Role overview

Own the machine learning stack for systems that detect activity around secured land and sea perimeters. The work spans computer vision research, multi-sensor perception, data and training pipelines, and deployment of real-time models on edge devices, with a focus on reliable performance in operational environments.

Responsibilities - Prototype and evaluate modern machine learning approaches to practical perception and autonomy problems. - Build and maintain data, training, and evaluation pipelines; curate datasets to track model performance over time. - Train, optimize, and deploy deep learning models for real-time use on edge hardware. - Work with camera, systems, and data-labeling specialists to develop and assess models. - Profile deployed systems and improve inference speed, memory use, and power efficiency. - Provide technical guidance to less experienced machine learning engineers.

Requirements - At least six years developing, benchmarking, and optimizing machine learning algorithms on large datasets. - Graduate degree in machine learning, robotics, or computer science with a computer vision focus, plus an undergraduate degree in a related technical field. - Strong computer vision and deep learning skills, with production experience taking models from research through deployment. - C++ proficiency in Linux and Python experience with frameworks such as PyTorch, JAX, or TensorFlow. - Experience deploying with TensorRT and ONNX, optimizing inference across CPUs, GPUs, or NPUs, and maintaining automated tests. - Ability to run experiments and ablation studies, document findings, and tune systems for latency, memory, and power; eligible to obtain and maintain a U.S. Secret clearance.

Nice to have

Experience with detection, tracking, segmentation, depth estimation, model compression, visual odometry, SLAM, camera models, optical flow, or RGB-D/LiDAR fusion. Remote system troubleshooting and at least one year in a technical leadership role are also valued.