A/AI Research Engineer - E2 at Lockheed Martin

Department: MFC-Primary

Compensation

$81,100 – $150,500/yr

Setup: On-site
Location: Orlando, Florida
Type: Full-time
Level: mid
Posted: Sep 17, 2026

Description

Lockheed Martin is hiring an AI/ML Research Engineer to research and develop machine learning, deep learning, computer vision, signal processing, and control-systems technologies for semi- and fully autonomous defense and commercial systems. The role develops and tests object-detection and classification models, deploys neural networks on CPUs, GPUs, and edge devices, validates algorithms against real-time data, supports ground and flight testing, and collaborates with mission, motion-planning, simulation, and testing teams. Candidates need a relevant STEM degree, at least one year of relevant experience, US citizenship, interim clearance, and proficiency in MATLAB, Python, and C++.

Required Skills

MATLAB
Python
C++
PyTorch
TensorFlow
ONNX
Computer vision
Deep learning
Object detection
CUDA
FPGA programming

Additional Information

Minimum Education: Bachelor of Science in electrical engineering, mechanical engineering, aerospace engineering, physics, computer science, applied mathematics, or a similar STEM field
Required Previous Experiences: At least 1 year of relevant experience
Experience training and inferring modern deep learning neural networks for object detection and classification
Experience designing and analyzing object detection and classification networks with image augmentation and explainable AI
Experience using PyTorch, TensorFlow, and ONNX
Experience deploying neural networks on embedded or edge devices
Experience validating C++ algorithm simulations and hardware models with real-time data collections
Experience implementing and debugging AI/ML algorithms in real-time processing environments
Preferred Qualifications: Active Secret Clearance
Experience developing and analyzing computer vision algorithms
Skills in probability, statistics, and Design of Experiments
Experience deploying real-time algorithms across hardware platforms
Knowledge of model optimization techniques
Knowledge of hardware acceleration techniques
Familiarity with AMD Xilinx Vitis AI and FPGA programming
Experience with TensorRT, OpenVINO, or other inference optimization frameworks