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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Computer Vision Engineer - **Company:** DIFFRACTION, INC. - **Location:** Somerville, MA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Artificial Neural Networks, Computer Vision, C++ (Programming Language), Field-Programmable Gate Array (FPGA), Global Positioning Systems (GPS), Hyperspectral Imaging, Python (Programming Language), Sensor Fusion, Pytorch, Uncertainty Quantification (UQ), Deep Learning, Discretization, Information Technology, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT, NVIDIA Jetson, Software Version Control, Data Pipelines - **Published:** October 28, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=cc470f72cc8eaadc ## About the Role You have built multi-sensor fusion on real sensors and have shipped estimators and learned models that had to work on hardware, not just on benchmarks. You are comfortable with probabilistic models and deep learning in the same week. You want a problem nobody has solved yet and the hardware to test it on., * M.S. in Computer Science, Electrical Engineering, or a related engineering field. * 3+ years of industry experience developing and deploying computer vision or perception systems on real sensors. * Hands-on experience with multi-sensor measurement fusion: combining measurements from multiple imaging or onboard sensors into one estimate or image, using probabilistic and learned methods. * Hands-on experience with computational imaging: image de-noising, restoration, or reconstruction. * Strong grounding in estimation theory and inverse problems and hands-on experience with deep learning for imaging. * Experience designing experiments that hold up: ablations, baselines, uncertainty quantification. * Strong proficiency in Python and PyTorch (or equivalent deep learning framework), with working knowledge of C++. * Solid foundation in image formation, camera models, and calibration, and in the noise characteristics of imaging sensors. * Solid software engineering practices: version control, testing, and reproducible experiments. * This an on-site in Somerville, MA, alongside the hardware team. Preferred: * Background in aerospace, defense, robotics, or autonomous systems, ideally with space or airborne sensors. * Robotics state estimation: Kalman filters, factor graphs, fusing IMU, GPS, and lidar. * Physics-informed or differentiable optics models, or end-to-end optics-algorithm co-design. * Deploying models to edge or embedded platforms (e.g., TensorRT, ONNX Runtime, NVIDIA Jetson). * FPGA or hardware-accelerated inference (e.g., Vitis AI, HLS). * Non-visible imaging (e.g., infrared, multispectral, or hyperspectral) or event-based sensors. * Publications or open-source contributions in sensor fusion, computational imaging, or computer vision. ## Description We are hiring a Computer Vision Engineer to build the models at the center of our Quantum Vision System. Our photonics do part of the computation in light before it reaches the sensor and your models finish it. You will own the imaging pipeline from raw sensor output to actionable detections: physics-based reconstruction and de-noising, detection and tracking, and deployment on the embedded hardware that flies. You will work directly with our optical and opto-mechanical engineers to build an end-to-end model of the imaging chain, from photons to outputs, so optics and algorithms can be optimized together. The first systems you work on are headed to space., * Design, train, and deploy computer vision models for imaging and perception tasks like image reconstruction, detection, classification, and tracking tailored to our optical front end. * Develop de-noising and image-restoration methods (classical and learned) that are robust to sensor noise, radiation effects, low light, and optical aberrations. * Build a cohesive, end-to-end model of the imaging chain coupling physics-based optical and sensor models with neural networks to enable joint optimization of optics and algorithms. * Design and implement sensor fusion algorithms that combine camera data with other onboard sensors using probabilistic and learned approaches. * Integrate vision and fusion outputs into the system's real-time control loop, defining interfaces, latency budgets, and failure modes together with controls and embedded engineers. * Optimize and deploy models to resource-constrained edge hardware (e.g., embedded GPU, FPGA, or SoC), including quantization, pruning, and runtime profiling. * Build data pipelines and simulation tools for synthetic and lab-captured datasets, including calibration, labeling, and domain-gap analysis between simulation and hardware. * Define metrics and run hardware-in-the-loop experiments in the lab to validate algorithm performance on real sensor data. * Document designs and results clearly, and contribute to technical reviews and the team's shared codebase and best practices.