Software Development Engineer, ML Systems, Annapurna Labs

Amazon
New York, NY, USA2025-08-19ONSITE

About the job

You will join a dynamic team building and applying AI agents to simplify and accelerate customer adoption of Trainium and Inferentia. As an SDE II you will work with external and internal customers to identify the main obstacles and the opportunities to accelerate their adoption of the Neuron technology.

Responsibilities

Solve challenging technical problems, often ones not solved before, at every layer of the stack.

Design, implement, test, deploy and maintain innovative software solutions to transform service performance, durability, cost, and security.

Build high-quality, highly available, always-on products.

Research implementations that deliver the best possible experiences for customers.

Create metrics, implement automation and other improvements, and resolve the root cause of software defects.

Build high-impact solutions to deliver to our large customer base.

Participate in design discussions, code review, and communicate with internal and external stakeholders.

Work cross-functionally to help drive business decisions with your technical input. You will collaborate closely with a cross-functional team comprised of compiler, hardware, and ML engineers.

Work in a startup-like development environment, where you’re always working on the most important stuff.

Qualifications

Minimum

3+ years of non-internship professional software development experience

2+ years of non-intternship design or architecture (design patterns, reliability and scaling) of new and existing systems experience

Experience programming with at least one software programming language

Preferred

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

Hands-on technical experience working on the Generative AI space

1+ years in machine learning or other computational modeling environments with an emphasis on building and optimizing models for diverse hardware platforms