Reinforcement Learning (Infrastructure)

Description

The Amazon AGI SF Lab is focused on developing new foundational capabilities for enabling useful AI agents that can take actions in the digital and physical worlds. We’re enabling practical AI that can actually do things for us and make our customers more productive, empowered, and fulfilled.

The lab is designed to empower AI researchers and engineers to make major breakthroughs with speed and focus toward this goal. Our philosophy combines the agility of a startup with the resources of Amazon. By keeping the team lean, we’re able to maximize the amount of compute per person. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research.

In this role, you will work closely with research teams to design, build, and maintain systems for training and evaluating state-of-the-art agent models.

Our team works inside the Amazon AGI SF Lab, an environment designed to empower AI researchers and engineers to work with speed and focus. Our philosophy combines the agility of a startup with the resources of Amazon.

Key job responsibilities
* Develop cutting-edge training infrastructure to ensure large-scale reinforcement learning on LLMs runs highly efficient and robust.
* Work across the entire technology stack, including low level ML system, job orchestration and data management.
* Analyze, troubleshoot and profiling complex ML systems, identify and address performance bottlenecks.
* Work closely with researchers, conduct MLSys research to create new techniques, infrastructure, and tooling around emerging research capabilities.

Basic Qualifications

- PhD, or Master's degree and 3+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience debugging ML systems

Preferred Qualifications

- PhD in Computer Science, Machine Learning, or a related field, with a focus on ML System.
- Demonstrated experience in developing, implementing and debugging large scale ML systems.
- Experience with distributed system, Megatron, vLLM, Ray, and working with GPUs.
- Experience with patents or publications at top-tier peer-reviewed conferences or journals.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.