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Kalman Filter Estimation Jobs (NOW HIRING)

MVP Robotics

Robotics Software Engineer

Bradford, VT

State Estimate & Kalman Filter Sensor Fusion * Demonstrated hands-on experience with camera systems and diverse sensor modalities, including radar and other perception sensors (e.g., LiDAR) * Path ...

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Kalman Filter Estimation information

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$20K

$84.6K

$129.5K

How much do kalman filter estimation jobs pay per year?

As of Aug 9, 2026, the average yearly pay for kalman filter estimation in the United States is $84,586.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,000.00 and $98,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Kalman filter estimation engineer, and why are they important?

To excel as a Kalman Filter Estimation Engineer, you need a strong background in control theory, linear algebra, statistics, and typically a degree in electrical engineering, robotics, or applied mathematics. Familiarity with MATLAB, Python, C++, and simulation tools like Simulink, as well as experience implementing Kalman filters in real-time systems, is highly valued. Analytical thinking, problem-solving, and effective communication are important soft skills for collaborating with multidisciplinary teams and presenting complex results. These competencies ensure accurate state estimation and system reliability in applications such as navigation, robotics, and sensor fusion.

What is the difference between Kalman Filter Estimation vs Signal Processing Engineer?

AspectKalman Filter EstimationSignal Processing Engineer
CredentialsMathematics, control systems, engineering degreesElectrical engineering, computer science, or related fields
Work EnvironmentResearch labs, aerospace, robotics, automationCommunications, audio/video, telecommunications industries
Industry UsageNavigation, robotics, sensor fusionFiltering, data analysis, signal enhancement

Kalman Filter Estimation focuses on estimating the state of dynamic systems using mathematical models, often in robotics and navigation. Signal Processing Engineers design and implement algorithms to analyze and modify signals in various industries. While both roles involve data analysis and mathematical skills, Kalman Filter Estimation is specialized in state estimation for control systems, whereas Signal Processing Engineers work broadly on signal manipulation and enhancement.

What are some common challenges faced by professionals working in Kalman filter estimation, and how can they be addressed?

Professionals working in Kalman Filter Estimation often encounter challenges such as model inaccuracies, noisy sensor data, and computational limitations, especially in real-time applications. Addressing these issues typically involves rigorous sensor calibration, careful tuning of filter parameters, and choosing appropriate variants of the Kalman filter, such as the Extended or Unscented Kalman Filter for nonlinear systems. Collaboration with domain experts and regular validation against ground truth data also help ensure robust performance. Additionally, staying updated with the latest research and tools in estimation theory can provide innovative solutions to emerging challenges.

What is Kalman filter estimation?

Kalman Filter Estimation is a mathematical algorithm used to estimate the state of a dynamic system from a series of noisy measurements. It is widely applied in fields such as robotics, aerospace, and navigation to provide real-time, optimal estimates of unknown variables by combining predictions from a model with observed data. The filter works recursively, meaning it updates its estimates and uncertainties as new measurements become available. Its strength lies in efficiently handling uncertainties and noise in both the system model and the measurements.
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Kalman Filter Estimation jobs near you
Infographic showing various Kalman Filter Estimation job openings in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Hybrid job distribution, with an average salary of $84,586 per year, or $40.7 per hour.

Robotics Software Engineer

MVP Robotics

Bradford, VT

Full-time

Re-posted 27 days ago

Job description

Robotics Software Engineer

Overview

MVP Robotics is a boutique engineering firm born out of Dartmouth College's Thayer School of Engineering based in Bradford, VT. MVP's mission is to improve human safety and performance along with survivability through applied robotics. MVP's robotic tackling dummies have been instrumental in mitigating head injuries on the football field throughout the NFL and youth football programs. MVP's tactical robots offer live-fire shooting targets for training our nation's warfighters. MVP continues to develop next-generation training and operational tools to overcome the challenges of tomorrow.

Position Summary

MVP is seeking a motivated, organized, and skilled Robotics Software Engineer to develop novel capabilities and advanced autonomy for robotic systems. This role is based in our Bradford, VT headquarters, requires weekly in office work Monday – Friday.

What you'll do

  • Embedded systems
  • C/C++
  • Linux
  • Python
  • ROS/ROS2
  • SLAM
  • Visual Inertial Odometry
  • State Estimate & Kalman Filter Sensor Fusion
  • Demonstrated hands-on experience with camera systems and diverse sensor modalities, including radar and other perception sensors (e.g., LiDAR)
  • Path planning
  • Obstacle Detection/Collision Avoidance
  • Lidar/Radar/Stereo Vision
  • Machine Learning/Deep Learning
  • (Preferred) Network architecture & communications
  • (Preferred) Multi-Agent autonomy

We require…

  • Minimum Bachelor's degree
  • 2+ Years experience in industry (internship/co-ops considered)
  • Effective Communicator

MVP Robotics is an equal-opportunity employer and is committed to providing a workplace free from harassment and discrimination. We are committed to recruiting, hiring, training and promoting qualified people of all backgrounds, and make all employment decisions without regard to any protected status.