Machine Learning Engineer - SageMaker AI Inference

Description

Demand for large language model (LLM) inference is growing fast, and the engineers who make it faster and more efficient are shaping the future of AI at Amazon. We are looking for a Software Development Engineer to focus on inference performance optimization, kernel tuning, and LLM serving efficiency. You will work across the full software development lifecycle to design, build, and measure systems that reduce latency and improve throughput for production inference workloads. This is a hands-on engineering role where your work directly translates into better performance for internal teams and, ultimately, for Amazon's customers. If you are an engineer who enjoys profiling bottlenecks, writing high-performance code, and finding elegant solutions to hard systems problems, we want to talk to you.

Key job responsibilities
- Design and implement kernel-level optimizations and inference pipeline improvements that reduce latency and increase throughput for LLM serving workloads.
- Profile, benchmark, and tune model inference performance across hardware and software stacks, identifying bottlenecks and delivering measurable gains.
- Write well-organized, maintainable code and participate actively in code reviews, providing constructive feedback to peers and mentoring newer engineers on the team.
- Collaborate with applied scientists, ML engineers, and infrastructure teams to translate model requirements into production-ready inference solutions.
- Evaluate emerging tools, frameworks, and generative AI techniques for inference optimization, recommending adoption where they improve efficiency or quality.

A day in the life
You might start your morning reviewing benchmark results from an overnight profiling run, then dig into a kernel implementation to shave milliseconds off a critical inference path. After lunch you could pair with an applied scientist to understand a new model architecture's compute profile and plan your optimization approach. Throughout the day you will participate in code reviews, document your design decisions, and share findings with the broader team so that improvements compound across projects.

About the team
Our team owns SageMaker AI inference at Amazon, and inference performance optimization is becoming increasingly important as LLM workloads scale. Performance optimization is one of our key differentiators, and every improvement we ship has a direct impact on cost, speed, and the experience our customers receive. We value collaboration, technical depth, and a culture where every engineer's perspective is heard. If you want to solve hard systems problems in an environment that rewards curiosity and rigorous engineering, this is the team for you.

Basic Qualifications

- 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
- 1+ years of software development engineer or related occupational experience
- 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
- 1+ years of Object Oriented Design experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
- Experience programming with at least one software programming language

Preferred Qualifications

- Experience with CUDA kernels or ML/low-level kernels
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.