At AWS AI, we want to make it easy for our customers to train their deep learning workload in the cloud. With Amazon SageMaker, AWS is building customer-facing services to empower data scientists and software engineers in their deep learning endeavors. As our customers rapidly adopt LLMs and Generative AI for their business, we’re building the next-generation AI platform to accelerate their development. We’re seeking a dedicated engineering team lead to drive building our next-generation AI compute platform that’s optimized for LLMs and distributed training.

As an SDE, you will be responsible for designing, developing, testing, and deploying distributed machine learning systems and large-scale solutions for our world-wide customer base. In this, you will collaborate closely with a team of ML scientists and customers to influence our overall strategy and define the team’s roadmap. You'll assist in gathering and analyzing business and functional requirements, and translate requirements into technical specifications for robust, scalable, supportable solutions that work well within the overall system architecture. You will also drive the system architecture, spearhead best practices that enable a quality product, and help coach and develop junior engineers. A successful candidate will have an established background in engineering large scale software systems, a strong technical ability, great communication skills, and a motivation to achieve results in a fast paced environment.

About You:
You are passionate about building platform and products for large scale deep learning model training (100+ billion parameter GPT, 1000s of GPU devices). You have a proven track record of bringing innovative research to customers. You are able to thrive and succeed in an entrepreneurial environment and not be hindered by ambiguity or competing priorities. Ownership, delivering results, thinking big and analytical leadership are essential to success in this role.

You have solid experience in multi-threaded asynchronous C++/Go development. You have prior experience in resource orchestrators with kubernetes, high performance computing, building scalable systems, experience in large language model training.

This is a great team to come to have a huge impact on AWS and the world's customers we serve!


Key job responsibilities
As a Software Development Engineer in the SageMaker HyperPod team, you will be responsible for:
- Developing innovative solutions for supporting Large Language Model training in a cluster of nodes;
- Develop and maintain a performant, resilient and fully-managed service built to train large-scale foundation models.
- Optimizing distributed training by profiling, identifying bottlenecks and addressing them by improving compute and network performance, as well as finding opportunities for better compute/communication overlap;
- You will serve as a key technical resource in the full development cycle, from conception to delivery and maintenance.
- You will own delivery of entire piece of the system and serve as technical lead on complex projects using best practice engineering standards
- Hire/mentor junior development engineers

A day in the life
Every day will bring new and exciting challenges on the job while you:

* Build and improve next-generation AI platform using Kubernetes as orchestration layer.
* Collaborate with internal engineering teams, leading technology companies around the world and open source community - PyTorch, NVIDIA/GPU
* Create innovative products to run at scale on the AI platform, and see them launched in high volume production



About the team
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.

Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

Mentorship & 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.

Hybrid Work
We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our US Amazon offices. Our hybrid models allow you the freedom to work from home whenever in-office collaboration isn’t necessary.