Principal AI & Optimization Engineer (Hybrid)

Prattwhitney; East Hartford (CT); Hybrid; USD 107,500 - 204,500; Full time

Benefits offered by this job

Medical, dental, vision

401(k) match

Flexible work schedules

Parental leave

Paid time off

Job summary

RTX in Hartford, CT is seeking a senior AI researcher to lead optimization and ML efforts for aerospace and defense applications. You will design and deploy ML solutions for complex scheduling and decision problems, leveraging discrete optimization, DL for vision, and multimodal models.

Collaborate with industry, academia, and government, publish results, and contribute to IP while working in a hybrid environment with onsite and offsite tasks.

Qualifications

• Ph.D. in Operations Research, Computer Science, Applied Mathematics, or a related field.
• Minimum 5 years of industrial experience.
• Expertise in one or more of: discrete optimization/mixed-integer programming; deep neural networks for vision; LLMs and vision-language integration.

Responsibilities

• Provide technical expertise in discrete optimization, deep learning for computer vision, LLMs, and VLMs for operations planning and scheduling in aerospace/defense.
• Design and develop optimized ML solutions for complex decision-making problems.
• Develop and deploy deep-learning models for challenging computer-vision tasks.
• Explore and integrate LLMs and VLMs for multimodal tasks.
• Lead and support sponsored programs and proposals; write research proposals.
• Build relationships with industry, academia, and government.
• Disseminate results via reports, conferences, and peer-reviewed articles; develop IP.

Skills

Discrete optimization

Deep learning for vision

Vision-language models

Research leadership

Education

PhD in Operations Research/CS/Applied Math

Tools

Python

MATLAB

C++

Git

Job description

RTX in Hartford, CT is seeking a senior AI researcher to lead optimization and ML efforts for aerospace and defense applications. You will design and deploy ML solutions for complex scheduling and decision problems, leveraging discrete optimization, DL for vision, and multimodal models.

Collaborate with industry, academia, and government, publish results, and contribute to IP while working in a hybrid environment with onsite and offsite tasks.