Amazon Software Engineer - Generative AI AGIF Runtime Services 

United States, Massachusetts, Boston 

149172210

Key job responsibilities
As a Software Development Engineer, you will be responsible for designing, developing, testing, and deploying high performance inference capabilities, including but not limited to multi-modality, SOTA model architectures, latency, throughput, and cost. You will collaborate closely with a team of engineers and scientists to influence our overall strategy, and define the team’s roadmap. You will drive system architecture, spearhead best practices, and mentor junior engineers.A day in the life
You will read papers and consult with scientists to get inspiration of emerging techniques, and blend those into our roadmap; You will design and experiment with new algorithms, benchmark the latency and accuracy of your implementations; Most importantly you will implement production grade solutions, and see them through the deployments swiftly; You may need to collaborate with other science and engineering teams to get things done properly; You will hold highest bar in operational excellence and support production systems, and constantly create solutions to minimize the ops load.

- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language
- Prior experience with software performance optimization Or Knowledge of Machine Learning and Deep Learning

- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
- Experience with Large Language Model inference
- Experience with Trainium and Inferentia Development
- Experience with GPU programming (TensorRT-LLM)
- Experience with Python, PyTorch, and C++ programming and performance optimization