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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 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 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?

AspectKalman Filtering Inertial NavigationINS Algorithm Developer
CredentialsEngineering degrees, knowledge of Kalman filters, navigation systemsEngineering degrees, expertise in navigation algorithms, sensor fusion
Work EnvironmentResearch labs, aerospace, defense, autonomous vehicle developmentSoftware development, simulation, embedded systems in similar industries
Industry UsageDesign and implementation of navigation systems using Kalman filtersDeveloping 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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Infographic showing various Kalman Filtering Inertial Navigation job openings in the United States as of May 2026, with employment types broken down into 1% Internship, 95% Full Time, 2% Temporary, 1% Contract, and 1% Nights. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution.
Senior Software Engineer, Inertial Navigation

Senior Software Engineer, Inertial Navigation

Waymo

Mountain View, CA • On-site, Remote

$204K - $259K/yr

Other

Posted 13 days ago

Job description

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver-The World's Most Experienced Driver-to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo's fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.

Hardware Engineering is a diverse, innovative, and collaborative group of electrical, mechanical, reliability, software and vehicle engineers. We design, build, and perfect the products which are the eyes and ears of Waymo's autonomous driving technology, and integrate those products into vehicle platforms. We're seeking curious and talented teammates to keep us moving in the right direction.

You will:

  • Develop algorithms to improve the accuracy and robustness of our core state estimation algorithm.
  • Engineer and deploy robust, production-quality C++ software to fuse data from inertial, GNSS, and other advanced sensors into our algorithm for both new and existing vehicle platforms.
  • Build and maintain tools, pipelines, and evaluation frameworks to rigorously assess positioning system performance across millions of miles of real-world driving data.
  • Leverage Waymo's massive datasets to conduct large-scale analyses and optimizations, fine-tune sensor models, characterize system performance, identify root causes of failures, and establish performance guarantees.
  • Collaborate closely within a multi-disciplinary team of engineers to solve complex technical challenges, contribute to a culture of high standards, and share your expertise with fellow team members.

You have:

  • MS, PhD or equivalent industry experience.
  • Expertise in state estimation theory and application, including Kalman Filters, particle filters, factor graphs, and nonlinear optimization techniques.
  • Deep understanding of inertial navigation principles, including sensor modeling, IMU error characterization, calibration, and multi-sensor fusion with GNSS and other aiding sensors.
  • Strong C++ skills and ability to write high quality production level code.

We prefer:

  • Previous experience in the autonomous vehicle industry, particularly in inertial navigation, localization, perception, or sensor fusion.
  • Experience with computer vision, visual inertial odometry, SLAM, ICP or other sensor modalities.
  • Experience developing and using tools for large-scale data analysis and visualization (e.g., SQL, Python with libraries like NumPy, Pandas, Matplotlib).
  • Familiarity with machine learning techniques applied to sensor fusion, sensor calibration, or positioning problems.

The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.

Waymo employees are also eligible to participate in Waymo's discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.

Salary Range
$204,000—$259,000 USD

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