• Co-develop, support and drive Raytheon AM technology roadmap • Work with programs early in development to assess the feasibility of AM implementation including design and cost considerations • Support multiple programs at a time to facilitate development, fabrication and qualification of AM hardware • Capture and execute Contracted Research and Development (CRAD) and Internal Research and Development (IRAD) to mature AM technology • Work with Quality, Operations and Supply Chain teams to establish a robust network of AM sources • Facilitate the creation of Technical Data Packages that effectively convey design intent as well as functional and quality requirements • Lead process development activity to improve print capability, increase speed, reduce cost and enhance material performance • Mature Raytheon's design for AM capability, including evaluation of available CAD tools and implementation of topology optimization workflows • Train and mentor other design engineers on basic principles of DFAM as well as specific considerations for Raytheon product families • Collaborate with government, industry, academic, and RTX Business Unit partners to develop whitepapers, project concepts, and responsive proposals

• Develop algorithms for AI/DL, data analytics, machine learning, or scientific computing • Contribute and advance open source NeMo-RL , Megatron Core , NeMo Framework • Solve large-scale, end-to-end AI training and inference challenges, spanning the full model lifecycle from initial orchestration, data pre-processing, running of model training and tuning, to model deployment. • Work at the intersection of computer-architecture, libraries, frameworks, AI applications and the entire software stack. • Innovate and improve model architectures, distributed training algorithms, and model parallel paradigms. • Performance tuning and optimizations, model training and finetuning with mixed precision recipes on next-gen NVIDIA GPU architectures. • Research, prototype, and develop robust and scalable AI tools and pipelines.

• Implement language and multimodal model inference as part of NVIDIA Inference Microservices (NIMs). • Contribute new features, fix bugs and deliver production code to TRT-LLM, NVIDIA’s open-source inference serving library. • Profile and analyze bottlenecks across the full inference stack to push the boundaries of inference performance. • Benchmark state-of-the-art offerings in various DL models inference and perform competitive analysis for NVIDIA SW/HW stack. • Collaborate heavily with other SW/HW co-design teams to enable the creation of the next generation of AI-powered services.

• Build and maintain reliable, scalable data pipelines in collaboration with more experienced data engineers. • Assist with data modeling (raw, processed, and consumable layers). • Monitor data loads and respond to data incidents. • Support documentation of the data team's processes and best practices. • Collaborate with analysts and product and engineering teams to understand data requirements.