• Research, design, and develop alternative PNT solutions leveraging signals of opportunity (Wi-Fi, cellular, LEO satellites, GNSS backups, etc.), radio SLAM, and inertial navigation. • Implement and optimize sensor fusion algorithms integrating IMU, LiDAR, radar, barometers, magnetometers, and RF-based positioning. • Develop robust navigation algorithms based on Kalman filtering, factor graphs, and particle filtering for real-time applications. • Work on radio-based navigation techniques, including time-difference-of-arrival (TDOA), frequency-difference-of-arrival (FDOA), and Doppler-based positioning. • Design and implement signal processing techniques for extracting positioning information from RF signals, including waveform analysis and multipath mitigation. • Conduct real-world testing and data collection campaigns to validate and refine PNT solutions. • Write clean, efficient, and well-documented code in Python and C++, with experience in simulation environments such as MATLAB, GNU Radio, or SDR-based platforms. • Collaborate with cross-functional teams, including RF engineers, AI/ML researchers, and roboticists, to develop integrated navigation systems.

• Manage the complete lifecycle of data for researchers - from sourcing and large-scale processing to delivering datasets that power our models. • Work with over a million hours of video and audio data. • Build the world’s best human-centric data lake by collaborating closely with our model training teams. • Extract new features and annotations that elevate our datasets. • Enhance model performance through high-quality, accurate datasets.

• Focus on research and development related to the optimization of ML models on GPU’s or AI accelerators • Use judgment in complex scenarios and apply optimization techniques to a wide variety of technical problems • Research, prototype and evaluate state of the art model optimization techniques and algorithms • Characterize neural network quality and performance based on research, experiment and performance data and profiling • Incorporate optimizations and model development best practices into existing ML development lifecycle and workflow • Define the technical vision and roadmap for DL model optimizations • Write technical reports indicating qualitative and quantitative results to colleagues and customers • Develop, deploy and optimize deep learning (DL) models on various GPU and AI accelerator chipsets/platforms

• Work collaboratively to improve our models and iterate on novel research directions • Apply and extend the Helm proprietary algorithmic toolkit for unsupervised learning and perception problems at scale • Carefully execute development and maintenance of tools used for deep learning experiments designed to provide new functionality for customers or address relevant corner cases in the system as a whole • Work closely with software and autonomous vehicle engineers to deploy algorithms on internal and customer vehicle platforms