Expoint - all jobs in one place

המקום בו המומחים והחברות הטובות ביותר נפגשים

Limitless High-tech career opportunities - Expoint

Amazon Sr Software Engineer- AI/ML AWS Neuron Apps 
United States, California, Cupertino 
1690192

16.09.2024
DESCRIPTION

AWS Neuron is the complete software stack for the AWS Inferentia and Trainium cloud-scale machine
The ML Apps team works side by side with chip architects, compiler engineers and runtime engineers to create , build and tune distributed training solutions with Trn1. Experience training these large models using Python is a must. FSDP, Deepspeed and other distributed training libraries are central to this and extending all of this for the Neuron based system is key.*Utility Computing (UC)*
**Why AWS
**Diverse Experiences**
Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.**Work/Life Balance* *
**Inclusive Team Culture* *
**Mentorship and Career Growth**
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.Key job responsibilitiesWork/Life Balance
Mentorship & Career Growth

BASIC QUALIFICATIONS

- 5+ years of programming using a modern programming language such as Java, C++, or C#, including object-oriented design experience
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Fundamentals of Machine learning and deep learning models, their architecture, training and inference lifecycles along with work experience on some optimizations for improving the model execution.


PREFERRED QUALIFICATIONS

- Master's degree in computer science or equivalent