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.