Perception Technical Lead

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 The Boring Company. 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 Technical Lead

We are looking for a domain expert to lead design, development, and evaluation for perception, marrying classical approaches and machine learning to drive optimal performance. We are building a perception stack for the home. This system must be able to provide full contextual awareness by reliably identifying threats in all environmental conditions. False positives lead to a terrible customer experience, while false negatives detract from our goal to provide absolute peace of mind. This role will also be highly collaborative with our hardware team, as we develop sensing requirements to guide off-the-shelf component selection and in-house hardware iteration.

We Value

Collaboration, pair programming, and teamwork.

Making small improvements and shipping code to production continuously.

Taking ownership across the stack (frontend and backend).

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

Leading 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.

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.

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

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