Formulate and solve estimation problems using Kalman filtering, Bayesian inference, factor graphs ... Strong understanding of inertial navigation, sensor calibration, and multi-modal sensor fusion (IMU ...
Formulate and solve estimation problems using Kalman filtering, Bayesian inference, factor graphs ... Strong understanding of inertial navigation, sensor calibration, and multi-modal sensor fusion (IMU ...
SLAM Architect (Robotics)
Seattle, WA · On-site
$63 - $86.25/hr
... navigation systems. This role involves creating innovative solutions for Visual-Inertial SLAM ... Qualifications: * Advanced knowledge of state estimation, Kalman filters, and factor graphs.
SLAM Architect (Robotics)
Seattle, WA · On-site
$63 - $86.25/hr
... navigation systems. This role involves creating innovative solutions for Visual-Inertial SLAM ... Qualifications: * Advanced knowledge of state estimation, Kalman filters, and factor graphs.
SLAM Architect (Robotics)
Seattle, WA · On-site
$63 - $86.25/hr
... navigation systems. This role involves creating innovative solutions for Visual-Inertial SLAM ... Qualifications: * Advanced knowledge of state estimation, Kalman filters, and factor graphs.
SLAM Architect (Robotics)
Seattle, WA · On-site
$63 - $86.25/hr
... navigation systems. This role involves creating innovative solutions for Visual-Inertial SLAM ... Qualifications: * Advanced knowledge of state estimation, Kalman filters, and factor graphs.
Kalman Filtering Inertial Navigation information
What are some common challenges faced when implementing Kalman Filtering for inertial navigation systems?
One common challenge in this role is managing sensor noise and drift, which can significantly affect the accuracy of inertial navigation solutions. You'll need to carefully tune filter parameters and sometimes integrate additional sensor data (like GPS or magnetometers) to improve robustness. Collaboration with hardware teams is typical, as understanding sensor characteristics is crucial for optimal filter performance. Additionally, real-time processing constraints often require you to optimize algorithms for efficiency without sacrificing accuracy.
What are the key skills and qualifications needed to thrive as a Kalman Filtering Inertial Navigation Engineer, and why are they important?
To thrive as a Kalman Filtering Inertial Navigation Engineer, you need a solid background in control systems, signal processing, and estimation theory, generally supported by a degree in electrical engineering, aerospace engineering, or a related field. Proficiency with MATLAB, Python, sensor fusion algorithms, and experience implementing Kalman filters in embedded systems are typically required. Strong analytical thinking, attention to detail, and effective communication skills help professionals collaborate across multidisciplinary teams and solve complex problems. These skills and qualifications are vital to ensure accurate navigation solutions and robust system performance in real-world applications.
What is Kalman Filtering in inertial navigation?
Kalman Filtering in inertial navigation refers to the use of a mathematical algorithm—the Kalman filter—to estimate the position, velocity, and orientation of a moving object by processing data from inertial sensors such as accelerometers and gyroscopes. The Kalman filter improves the accuracy of navigation by optimally combining sensor measurements and correcting for errors and noise. This technique is essential in applications like aerospace, robotics, and autonomous vehicles, where precise movement tracking is required. By continuously updating estimates as new sensor data arrives, Kalman filtering helps provide reliable and real-time navigation solutions.
What is the difference between Kalman Filtering Inertial Navigation vs INS Algorithm Developer?
| Aspect | Kalman Filtering Inertial Navigation | INS Algorithm Developer |
|---|---|---|
| Credentials | Engineering degrees, knowledge of Kalman filters, navigation systems | Engineering degrees, expertise in navigation algorithms, sensor fusion |
| Work Environment | Research labs, aerospace, defense, autonomous vehicle development | Software development, simulation, embedded systems in similar industries |
| Industry Usage | Design and implementation of navigation systems using Kalman filters | Developing algorithms for inertial navigation systems, improving accuracy |
Kalman Filtering Inertial Navigation focuses on applying Kalman filters to process sensor data for navigation accuracy, while INS Algorithm Developers design and optimize algorithms for inertial navigation systems. Both roles require similar technical skills but differ in their primary focus: one on filtering techniques, the other on algorithm development.
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What job categories do people searching Kalman Filtering Inertial Navigation jobs in Seattle, WA look for? The top searched job categories for Kalman Filtering Inertial Navigation jobs in Seattle, WA are:
Full-time
Medical, Dental, Vision, Retirement, PTO
Posted 6 days ago
Job description
About Overland AI
Founded in 2022 and headquartered in Seattle, Washington, Overland AI is transforming land operations for modern defense. The company leverages over a decade of advanced research in robotics and machine learning, as well as a field-test forward ethos, to deliver combined capabilities for unit commanders. Our OverDrive autonomy stack enables ground vehicles to navigate and operate off-road in any terrain without GPS or direct operator control. Our intuitive OverWatch C2 interface provides commanders with precise coordination capabilities essential for mission success.
Overland AI has secured funding from prominent defense tech investors including 8VC and Point 72, and built trusted partnerships with DARPA, the U.S. Army, Marine Corps, and Special Operations Command. Backed by eight-figure contracts across the Department of Defense, we are strengthening national security by iterating closely with end users engaged in tactical operations.
Role Summary
Overland AI is building autonomous ground vehicles capable of operating where GPS is unreliable, terrain is unpredictable, and failure is not an option. We're looking for a Technical Lead in State Estimation to define the algorithms that allow our vehicles to understand exactly where they are-and keep them operating confidently in the world's most demanding environments.
In this role, you'll lead the architecture and development of our state estimation stack, solving challenging problems across localization, mapping, sensor fusion, and probabilistic inference. You'll work at the intersection of cutting-edge robotics research and production autonomy, turning advanced estimation techniques into robust systems that perform reliably in real-world deployment.
As a technical leader, you'll set the direction for state estimation at Overland, mentor a team of exceptional robotics engineers, and collaborate across perception, planning, controls, and platform engineering to build the next generation of autonomous off-road vehicles.
Key Responsibilities
Minimum Qualifications
Desired Qualifications & Experience
Benefits
Overland AI believes in creating a work environment that you look forward to embracing every day.
Founded in 2022 and headquartered in Seattle, Washington, Overland AI is transforming land operations for modern defense. The company leverages over a decade of advanced research in robotics and machine learning, as well as a field-test forward ethos, to deliver combined capabilities for unit commanders. Our OverDrive autonomy stack enables ground vehicles to navigate and operate off-road in any terrain without GPS or direct operator control. Our intuitive OverWatch C2 interface provides commanders with precise coordination capabilities essential for mission success.
Overland AI has secured funding from prominent defense tech investors including 8VC and Point 72, and built trusted partnerships with DARPA, the U.S. Army, Marine Corps, and Special Operations Command. Backed by eight-figure contracts across the Department of Defense, we are strengthening national security by iterating closely with end users engaged in tactical operations.
Role Summary
Overland AI is building autonomous ground vehicles capable of operating where GPS is unreliable, terrain is unpredictable, and failure is not an option. We're looking for a Technical Lead in State Estimation to define the algorithms that allow our vehicles to understand exactly where they are-and keep them operating confidently in the world's most demanding environments.
In this role, you'll lead the architecture and development of our state estimation stack, solving challenging problems across localization, mapping, sensor fusion, and probabilistic inference. You'll work at the intersection of cutting-edge robotics research and production autonomy, turning advanced estimation techniques into robust systems that perform reliably in real-world deployment.
As a technical leader, you'll set the direction for state estimation at Overland, mentor a team of exceptional robotics engineers, and collaborate across perception, planning, controls, and platform engineering to build the next generation of autonomous off-road vehicles.
Key Responsibilities
- Design and implement odometry, localization, and mapping algorithms that enable reliable autonomy in GPS-denied and degraded environments.
- Develop robust multi-sensor fusion systems combining IMUs, LiDAR, cameras, GNSS, wheel encoders, and other onboard sensors.
- Formulate and solve estimation problems using Kalman filtering, Bayesian inference, factor graphs, nonlinear optimization, and modern probabilistic techniques.
- Evaluate and integrate learned approaches-including learned odometry, feature representations, and neural mapping methods-where they deliver measurable improvements over classical techniques.
- Develop high-performance, production-quality C++ (C++23) software optimized for real-time robotic systems.
- Build tooling, simulation infrastructure, and evaluation pipelines that enable rapid algorithm development and validation using large-scale field datasets.
- Lead verification and validation efforts across diverse terrain, weather conditions, and operational environments.
- Partner closely with perception, planning, controls, and systems engineers to deliver an integrated, reliable autonomy stack.
- Mentor engineers, drive technical excellence, and establish best practices for robotics software development.
Minimum Qualifications
- MS or PhD in Robotics, Computer Science, Electrical Engineering, or a related technical field with specialization in state estimation, SLAM, localization, or probabilistic inference.
- 5+ years developing production-grade state estimation or SLAM systems deployed on physical robotic platforms.
- Deep expertise in probability theory, Bayesian estimation, optimization, and nonlinear inference, including:
-
- EKF, UKF, Error-State Kalman Filters
- Factor graph optimization (GTSAM, Ceres, g2o)
- MAP/MLE estimation
- Demonstrated experience deploying robust estimation systems in complex, unstructured, or off-road environments.
- Expert-level C++ and strong Python development skills.
- Experience building low-latency, real-time robotics software.
- Strong understanding of inertial navigation, sensor calibration, and multi-modal sensor fusion (IMU, LiDAR, cameras, GNSS).
- Publications or significant technical contributions in SLAM, visual-inertial odometry, LiDAR-inertial odometry, localization, or related fields.
Desired Qualifications & Experience
- PhD in Robotics, Computer Science, Electrical Engineering, or a related field.
- Experience incorporating machine learning into estimation pipelines.
- Experience with ROS 2 and real-time middleware (DDS, shared-memory transport).
- Experience with terrain-relative navigation, prior-map localization, or GPS-denied navigation.
- Contributions to widely used open-source robotics or estimation libraries.
- Experience leading technical teams, mentoring engineers, and driving architecture decisions.
- Experience shipping autonomy systems on production robotic or autonomous vehicle platforms.
Benefits
Overland AI believes in creating a work environment that you look forward to embracing every day.
- The salary range for this position is $200K to $260K annually
- Equity compensation
- Best-in-class healthcare, dental and vision plans.
- Flexible PTO
- 401k with company match
- Parental leave