Senior Perception Software Engineer

Who We Are Sauron is the home security company of the future. Homeowners today lack compelling options when it comes to peace of mind against vulnerabilities, and total command and control of their home; there is no definitive, protective brand in the space. Leveraging cutting-edge AI, sensor technology, and nonlethal deterrence, Sauron brings next-generation technology to homeowners to protect their families and property. Incubated by the serial entrepreneur Kevin Hartz and Atomic, Sauron has raised an $18M seed round from leading venture capital firms and angel investors, including 8VC and Flock Safety CEO Garret Langley, to build the new perception system for the home. The Role | Senior Perception Software Engineer This role involves model design, development, and evaluation for perception tasks that include both classic computer vision approaches and more modern ML approaches. Our hardware will operate around the home and must be able to complete its mission reliably in the face of all environmental conditions. We aim to build a system that handles a wide variety of scenarios and do so without error. This role will be highly collaborative with our hardware team to develop requirements needed for sensing, tracking, and other perception tasks, as well as iteration for various generations of hardware. We Value Collaboration, pair programming, and teamwork. Making small improvements and shipping code to production continuously. Taking ownership across the stack. Test-driven development, and refactoring regularly to keep our codebases healthy. You Will Contribute By Extracting the maximum value from our sensors, fusing all observations available while being robust to occlusions, poor lighting and disguises Assisting in the collection, labeling, and management of datasets to train and evaluate ML models. Leveraging state of the art models for 3D object detection, tracking, facial recognition and semantic scene understanding, and push them to the limits of their performance in this problem domain. Analyzing the performance of systems both in simulation and using data from deployments in the field to find headroom and devise solutions to reduce it. Probing the inner workings of neural networks to uncover and mitigate edge case failures. Contributing to machine learning infrastructure (e.g. distributed training, continuous model integration, data management, and evaluation of production systems). Your Background Includes 4 years of professional experience with machine learning for hardware products in a safety-critical field, e.g. aerospace, robotics, medical devices, autonomous vehicles. Passionate about ML, both robust engineering and research challenges. An understanding of the theory and practice of modern machine learning techniques. A clear grasp of basic linear algebra, optimization, statistics, and algorithms. Experienced at facets of training and using deep-learning models, including writing custom layers/operations, optimizing networks for inference on edge compute, reproducibility and evaluation. Experience working with Pytorch, Tensorflow or other modern deep learning frameworks. Familiar with the use of VLMs and other multi-modal models for semantic scene understanding and description. Able to solve complex problems with little supervision. Excellent communicator, both written and verbal. A generalist mindset and can dive in wherever the bottlenecks are, whether that be spooling up cloud compute services to optimizing for embedded systems. Nice to Have Experience building high-performance software systems using compiled languages (C/C++/Rust/etc.). Experience with Middleware frameworks such as ROS. Experience with build systems such as Bazel, CMake. Experience in GPU architecture and CUDA programming.