Computer Vision Engineer - UAS

Job Summary :

T3i builds mission-critical unmanned systems designed to improve U.S. forces' operational effectiveness and battlefield lethality. Our Blackfoot platform is a tactical FPV drone system engineered for reliability, adaptability, survivability, and rapid deployment in demanding operational environments.

Job Summary :

T3i builds mission-critical unmanned systems designed to improve U.S. forces' operational effectiveness and battlefield lethality. Our Blackfoot platform is a tactical FPV drone system engineered for reliability, adaptability, survivability, and rapid deployment in demanding operational environments.

We operate in tight feedback loops between design, build, test, and field use, where every flight directly informs the next iteration of the platform. We are a small, fast-moving team that values ownership, technical depth, execution, and mission focus.

We are seeking a Computer Vision Engineer (CVE) to develop the Blackfoot’s perception and visual autonomy stack. This role is critical to delivering robust onboard vision capabilities - object detection, tracking, visual-inertial navigation, and last-mile terminal guidance - that perform reliably in degraded, GPS-denied, and EW-contested environments.

The CVE will own the design, training, optimization, and deployment of computer vision models running on resource-constrained embedded hardware, working directly with flight software, autonomy, and hardware teams to translate algorithms into deployed capability.

Position Summary:

The CVE will lead the development of real-time perception algorithms for the Blackfoot platform, including detection, classification, tracking, visual odometry, and target lock / terminal guidance. This role emphasizes building models and pipelines that are not only accurate, but small, fast, and resilient under real-world operating conditions - motion blur, low light, adverse weather, occlusion, and adversarial RF/visual environments.

The ideal candidate is equally comfortable writing custom CV algorithms from scratch, training and optimizing neural networks, integrating models onto embedded computers (Jetson, Hailo, Ambarella, or similar), and iterating against real flight data captured by our test pilots. Strong classical computer vision fundamentals - camera calibration, multi-view geometry, feature matching, and bundle adjustment - are as important as modern deep learning.

This role is ideal for someone who thrives in fast-paced R&D environments where models are deployed to airframes within days, not quarters, and where flight-test feedback directly drives the next training cycle.

Primary Responsibilities:

• Design, train, evaluate, and deploy computer vision models for detection, classification, segmentation, tracking, and re-identification of ground and aerial targets
• Develop and harden visual-inertial odometry (VIO), visual SLAM, and GPS-denied navigation capabilities for tactical FPV operations
• Build and maintain target lock, terminal guidance, and last-mile autonomy perception pipelines aligned with operational mission requirements
• Write custom computer vision algorithms from scratch where library implementations don't meet platform constraints (real-time, embedded, adversarial conditions)
• Own camera and sensor integration: calibration workflows, intrinsic/extrinsic estimation, multi-sensor synchronization, and image pipeline development across RGB, thermal/IR, and stereo modalities
• Optimize models for real-time inference on embedded edge compute platforms (NVIDIA Jetson, Hailo, Ambarella, Qualcomm, or similar) using TensorRT, ONNX, quantization, pruning, and distillation techniques
• Write production-quality C++ and Python inference code integrated with onboard flight software and autonomy stacks; apply GPU/CUDA programming for accelerated processing where required
• Curate, label, and manage flight-collected datasets; build data pipelines for ingestion, augmentation, versioning, and retraining
• Establish evaluation frameworks and validate perception capabilities across the full test stack: unit tests, simulation, software-in-the-loop, hardware-in-the-loop, and live flight testing
• Collaborate with autonomy, flight…