Senior Computer Vision Engineer

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.

• 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.

• 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 Tensor Flow.
• 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.

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.