You will be an Algorithm Engineer on the Lockheed Martin Aeronautics Sensor Fusion team, working at the forefront of sensor fusion, perception, and autonomy for fighter and unmanned platforms. In this role you will have a significant impact on shaping the future of Sensor Fusion across Aeronautics, defining the vision for our next‑generation platforms, and integrating Lockheed Martin’s most advanced technologies. If you thrive on pushing the envelope, enjoy tackling difficult algorithmic challenges, and are passionate about turning novel sensor concepts into flight‑ready software, this position on our Emerging‑Technology Sensor Fusion team is for you.
As an engineer working in sensor fusion you will possess a combination of skills from various disciplines:
Programming Fundamentals
Programming Languages: Proficiency in languages such as Python, C++, C#, or MATLAB.
Software Frameworks: Familiarity with software frameworks, such as NumPy, SciPy, or TensorFlow, MATLAB, MATLAB Object Oriented, for numerical computations.
Simulation Tools: Knowledge of simulation tools, such as Simulink or MATLAB, for modeling and testing sensor fusion systems.
Mathematical Foundations
Linear Algebra: Understanding of vector spaces, linear transformations, and matrix operations.
Probability Theory and Statistics: Familiarity with probability distributions, Bayes' theorem, and stochastic processes. Knowledge of statistical inference, estimation, and hypothesis testing.
Advanced topics: Understanding of numerical optimization, convex optimization, non-linear optimization, machine learning, and/or mathematical methods.
Signal Processing and Filtering
Signal Processing: Understanding of signal processing techniques, such as filtering, convolution, and Fourier analysis.
Kalman Filter: Familiarity with the Kalman filter algorithm, its variants (e.g., Extended Kalman Filter, Unscented Kalman Filter), and applications.
Particle Filter: Knowledge of particle filter algorithms and their applications in non-linear, non-Gaussian systems.
Tracking
Embedded Development: Acute proficiency in Programming Fundamentals applied to embedded systems.
Sensor Modeling: Understanding of sensor characteristics, noise models, calibration techniques, Signal Processing and Filtering, and Mathematical Foundations.
Data Association: Familiarity with data association techniques, such as nearest neighbor, joint probabilistic data association, and multiple hypothesis tracking.
State Estimation: Knowledge of state estimation algorithms, including Kalman filters, particle filters, and Bayesian estimation methods.
Sensor Integration and Management
Resource Allocation: Experience tasking, prioritizing, and scheduling of sensors and maximizing information value, system needs, or target tracking accuracy with a deep understanding of Programming Fundamentals.
Sensor Integration: Experience with integrating sensors like LIDAR, RADAR, or cameras and offboard sources.
