Senior Perception Engineer, Facial Recognition

Who We Are

Sauron protects your family and home, bringing the innovations of autonomous robots and self-driving cars to residential security. Our team is led by veteran entrepreneurs and roboticists, alumni of Zipline, Tesla, and Cloud Kitchens. Sauron has raised an $18M seed round led by A* and Atomic with participation from other leading venture capital firms and angel investors, including 8VC and Flock Safety CEO Garret Langley. 

The Role | Perception Engineer, Facial Recognition

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

• Leading development efforts on facial recognition systems.

• Extracting the maximum value from our sensors, fusing all observations available while being robust to occlusions, poor lighting and disguises

• Leading the collection, labeling, and management of datasets to train and evaluate ML models for facial recognition.

• Leveraging state of the art models for facial recognition and pushing them to the limits of their performance in this problem domain.

• Performing trade studies to understand the value of new sensors or processing approaches to system reliability.

• 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.

• Designing systems that can understand and communicate when they are not working well.

• Contributing to machine learning infrastructure (e.g. distributed training, continuous model integration, data management, and evaluation of production systems).
  

Your Background Includes

• 5+ 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.

• A deep understanding of the theory and practice of modern machine learning techniques.

• A clear grasp of basic linear algebra, optimization, statistics, and algorithms.

• Experienced at all facets of training and using deep-learning models, including writing custom layers/operations, optimizing networks for inference on edge compute, reproducibility and evaluation.

• Extensive 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.