Software Engineer-AI/ML, Inference Team - AWS Neuron
Amazon
- Location
- Onsite (Seattle, Washington)
- Compensation
- $143k - $194k/yr
- Employment
- Full-time
- Level
- Mid Level
Posted 2 days ago
About the Role
Join the AWS Neuron team to build core serving technologies for large-scale model inference on purpose-built accelerators. You will optimize performance in open-source frameworks like vLLM and SGLang, enabling faster deployment of new models for customers.
Skills
Software engineering
Machine learning
Inference optimization
C++
Java
C#
Perl
Distributed systems
Multi-threaded programming
vLLM
SGLang
Transformer architecture
CUDA
Triton
Performance profiling
System architecture
Benefits
- Health insurance
- Dental insurance
- Vision insurance
- 401(k) matching
- Paid time off
- Parental leave
- Life insurance
Perks
- Restricted stock units
- Sign-on payments
Full job details
AWS Neuron is the complete software stack for AWS Inferentia and Trainium — AWS purpose-built accelerators for cloud-scale machine learning. Join the Machine Learning Inference Applications team to build the serving technology that lets customers run large-scale model inference fast and efficiently on Neuron chips.
As an engineer on this team, you'll work on core serving technologies within open-source frameworks such as vLLM and SGLang, optimizing model serving performance on Neuron and broadening the range of models we support out of the box. Your work directly accelerates how quickly new models are enabled, shipped, and delivered to customers.
Key job responsibilities
- Contribute core serving features to open-source inference frameworks such as vLLM and SGLang — implementing and upstreaming support for continuous batching, paged attention, quantization, and distributed inference on Neuron.
- Broaden the range of models supported out of the box, and build tooling and automation that shortens the path from a new model to a production-ready deployment.
- Improve model development and shipping velocity by reducing enablement time for new models and strengthening the test, benchmarking, and release workflows the team relies on.
- Collaborate with model development, performance, compiler, and runtime engineers to deliver end-to-end model performance — production-ready accuracy, scalability, and efficiency across a broad range of models and customer workloads.
- Apply strong engineering practices — code reviews, testing, and operational excellence — to ship reliable, high-performance inference that customers depend on.
About the team
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future. Basic Qualifications: - 3+ years of non-internship professional software development experience
- 3+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- 2+ 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
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
- Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques Preferred Qualifications: - Experience in debugging, profiling, and implementing software engineering best practices in large-scale systems
- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
- Experience with vLLM, SGLang, TensorRT or similar platforms in production environments, or experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
- Kernel development experience (e.g., CUDA, Triton)
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.
USA, WA, Seattle - 143,700.00 - 194,400.00 USD annually
As an engineer on this team, you'll work on core serving technologies within open-source frameworks such as vLLM and SGLang, optimizing model serving performance on Neuron and broadening the range of models we support out of the box. Your work directly accelerates how quickly new models are enabled, shipped, and delivered to customers.
Key job responsibilities
- Contribute core serving features to open-source inference frameworks such as vLLM and SGLang — implementing and upstreaming support for continuous batching, paged attention, quantization, and distributed inference on Neuron.
- Broaden the range of models supported out of the box, and build tooling and automation that shortens the path from a new model to a production-ready deployment.
- Improve model development and shipping velocity by reducing enablement time for new models and strengthening the test, benchmarking, and release workflows the team relies on.
- Collaborate with model development, performance, compiler, and runtime engineers to deliver end-to-end model performance — production-ready accuracy, scalability, and efficiency across a broad range of models and customer workloads.
- Apply strong engineering practices — code reviews, testing, and operational excellence — to ship reliable, high-performance inference that customers depend on.
About the team
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future. Basic Qualifications: - 3+ years of non-internship professional software development experience
- 3+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- 2+ 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
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
- Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques Preferred Qualifications: - Experience in debugging, profiling, and implementing software engineering best practices in large-scale systems
- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
- Experience with vLLM, SGLang, TensorRT or similar platforms in production environments, or experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
- Kernel development experience (e.g., CUDA, Triton)
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.
USA, WA, Seattle - 143,700.00 - 194,400.00 USD annually