This role pays less than
99%
of similar roles. Most pay
$186,200–$257,515
— the shaded band above.
At the midpoint, this role pays about
$115k
versus about
$222k
for comparable roles.
Based on 240 similar postings.
Employer
About GE Aerospace
GE Aerospace (General Electric Aerospace) designs and produces commercial and military jet engines, components, and integrated systems for aircraft manufacturers and airlines worldwide. Industry: Aerospace & Aviation Engines
GE Aerospace currently has
40 open roles
on FindRole.
Listed pay typically runs
$130,000–$173,000
across 23 roles with salary data.
As a Lead Scientist - AI & Computer Vision - Aerospace Research, you will join a multidisciplinary team of scientists and engineers to develop software solutions for aerospace programs. You will design experiments to probe learning systems, build and refine AI/CV algorithms inspired by theory and data, and prototype novel systems moving from simulation to flight-relevant environments. Your daily work involves translating fundamental discoveries into scalable tools for manufacturing, testing, and lifecycle management while contributing to government-sponsored research with academic and federal partners. You will utilize Python to create both quick demonstrations and robust software systems. Key technical areas include Geometry in Computer Vision, CAD Registration, CT Imaging, Machine Learning plus Physics, Explainable Machine Learning, Graph Neural Networks, Causal Inference, Reinforcement Learning, and Vision Language Models. This role addresses complex challenges including physics discovery, uncertainty management, and edge environments.
Machine Learning
Generative AI
PyTorch
LLM
Python
Knowledge Graphs
Geometric Deep Learning
Multi-modal Learning
Data Science
Biostatistics
Graph Algorithms
Time Series Analysis
Statistical Analysis
Feature Engineering
Omics Data
Data Visualization
Machine Learning
Natural Language Processing
Large Language Models
Reinforcement Learning
Retrieval-Augmented Generation
RLHF
Direct Preference Optimization
Instruction Tuning
Fine-tuning
Statistical Inference
Experimental Design
Information Retrieval
Predictive Analytics
Agentic Systems