Principal State Estimation Engineer

Echodyne is seeking a Principal State Estimation Engineer to join our fast-growing team. 

ROLE OVERVIEW

In this role, you’ll lead development of pose-estimation capabilities for Echodyne radars operating in GPS-denied environments. You’ll architect and develop new on-radar systems that use radar and inertial sensor data to estimate the global position and motion of radars without reliance on external navigation systems. 

You’ll make key algorithm and system-level tradeoffs and own efforts from early prototyping through production deployment. You’ll also provide technical leadership on related state-estimation and sensor-fusion problems, such as target tracking. 

Your work will have an immediate real-world impact on our established and rapidly growing fleet of fielded radars, helping make them more robust and effective at protecting the infrastructure and people that rely on them in an evolving world. 

RESPONSIBILITIES

• Lead development of methods for estimating radar position, velocity, orientation, angular rate, and height above ground in GPS-denied environments using radar and IMU data, terrain maps, and other on-radar sensors and data. 
• Apply techniques from robotics and autonomy, including SLAM, odometry, inertial navigation, localization, factor graphs, probabilistic state estimation, and machine learning – within the constraints of an embedded real-time system. 
• Take these algorithms from research and prototyping through production implementation. 
• Drive system-level impacts and coordinate with other teams on deployment. 
• Work with radar, signal processing, embedded software, systems, and test teams to integrate capabilities into products. 
• Analyze field data to characterize performance and identify failure modes to produce robust, production-grade solutions. 

REQUIRED SKILLS / EXPERIENCE

• Demonstrated ability to independently lead an open-ended technical problem. 
• Experience developing state-estimation, localization, navigation, perception, or machine learning solutions for robotics, VR/AR headsets, autonomous vehicles, drones, aerospace, or similar platforms from prototype through production deployment. 
• Experience working with real-world spatial sensor data (e.g. camera, lidar, radar, ultrasound, sonar). 
• Experience with SLAM, Kalman filtering, Bayesian estimation, and factor graphs. 
• Deep understanding of state-estimation theory, 3D geometry, coordinate transformations, rotations, and kinematics. 
• Strong foundations in linear algebra, probability, statistics, and optimization. 
• Strong software development skills in C++ and either Python or MATLAB. 
• Strong verbal and written communication skills. 

DESIRED SKILLS / EXPERIENCE 
(Looking for one or more as a complement to the core skills) 

• Experience with visual, lidar, or radar odometry; map- or terrain-relative localization, Maximum Likelihood Estimation. 
• Experience with open-source C++ optimization libraries (GTSAM, Ceres). 
• Software development processes and best practices. 
• Real-time or embedded algorithm development. 
• Understanding of traditional navigation sensors (IMUs, accelerometers, gyroscopes, magnetometers, GPS, etc.). 
• Familiarity with alternative navigation sensors (camera, RF ranging, celestial, etc.). 

PREFERRED QUALIFICATIONS

• MS with 7+ years or PhD with 5+ years of relevant experience in robotics, VR/AR headsets, aerospace, electrical engineering, computer science, applied mathematics, or a related field. 
• Demonstrated technical leadership on complex, production-grade algorithm development efforts. 
• Ability to handle export-controlled (ITAR, EAR) data. 

Echodyne’s technology is export controlled by the U.S. Government and we must evaluate an applicant’s eligibility to handle export-controlled information or obtain required Government authorizations.  Therefore, we will ask you as part of the application process to identify whether you are a U.S. Citizen or green card holder, or have asylum/refugee status in the U.S. 

WHAT WE OFFER

This is an exempt role.