Perception 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 | Perception Engineer

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 involves model design, development, and evaluation for perception tasks that include both classic computer vision approaches and more modern ML approaches. 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.

What You Will Do

• Lead the collection, labeling, and management of datasets to train and evaluate ML models.

• Develop perception capabilities for our MVP that will ultimately become the product that allows us to reliably secure homes.

• Develop perception systems for object detection, segmentation, tracking, and higher-level perception tasks utilizing both 2D and 3D image data.

Who You Are

• Machine learning models for perception tasks, which can include detection and classification, pose estimation, tracking, fused sensor models and architecture. Experience in a few of these areas is ideal, all are not required.

• Experience with camera perception.

• Experience with model optimization, tuning, and evaluation.

• Experience with LiDAR and other sensor modalities.

• LLMs (including VLMs) for semantics and contextual information

• (nice to have) Sensor calibration (intrinsics and extrinsics)