Amazon
Machine learning engineer -AI/ML, AWS Neuron Inference, AWS Neuron Inference
Amazon, Seattle, Washington, us, 98127
Overview
Is your CV ready If so, and you are confident this is the role for you, make sure to apply asap. AWS Neuron is the complete software stack for the AWS Inferentia and Trainium cloud-scale machine-learning accelerators. This role is for a senior software engineer in the Machine Learning Inference Applications team. This role is responsible for development and performance optimization of core building blocks of LLM Inference - Attention, MLP, Quantization, Speculative Decoding, Mixture of Experts, etc. The team works side by side with chip architects, compiler engineers and runtime engineers to deliver performance and accuracy on Neuron devices across a range of models such as Llama 3.3 70B, 3.1 405B, DBRX, Mixtral, and so on.
Responsibilities
Adapt latest research in LLM optimization to Neuron chips to extract best performance from both open source as well as internally developed models.
Work across teams and organizations to deliver performance and accuracy on Neuron devices for models including Llama 3.3 70B, Llama 3.1 405B, DBRX, Mixtral, and related workloads.
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
2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
Programming proficiency in Python or C++ (at least one required)
Experience with PyTorch
Working knowledge of Machine Learning and LLM fundamentals including transformer architecture, training/inference lifecycles, and optimization techniques
Strong understanding of system performance, memory management, and parallel computing principles
PREFERRED QUALIFICATIONS
Experience with JAX
Experience with debugging, profiling, and implementing software engineering best practices in large-scale systems
Expertise with PyTorch, JIT compilation, and AOT tracing
Experience with CUDA kernels or equivalent ML/low-level kernels
Experience with performant kernel development (e.g., CUTLASS, FlashInfer)
Experience with inference serving platforms (vLLM, SGLang, TensorRT) in production environments
Deep understanding of computer architecture, operating systems, and parallel computing
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.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $129,300/year in our lowest geographic market up to $223,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
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Is your CV ready If so, and you are confident this is the role for you, make sure to apply asap. AWS Neuron is the complete software stack for the AWS Inferentia and Trainium cloud-scale machine-learning accelerators. This role is for a senior software engineer in the Machine Learning Inference Applications team. This role is responsible for development and performance optimization of core building blocks of LLM Inference - Attention, MLP, Quantization, Speculative Decoding, Mixture of Experts, etc. The team works side by side with chip architects, compiler engineers and runtime engineers to deliver performance and accuracy on Neuron devices across a range of models such as Llama 3.3 70B, 3.1 405B, DBRX, Mixtral, and so on.
Responsibilities
Adapt latest research in LLM optimization to Neuron chips to extract best performance from both open source as well as internally developed models.
Work across teams and organizations to deliver performance and accuracy on Neuron devices for models including Llama 3.3 70B, Llama 3.1 405B, DBRX, Mixtral, and related workloads.
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
2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
Programming proficiency in Python or C++ (at least one required)
Experience with PyTorch
Working knowledge of Machine Learning and LLM fundamentals including transformer architecture, training/inference lifecycles, and optimization techniques
Strong understanding of system performance, memory management, and parallel computing principles
PREFERRED QUALIFICATIONS
Experience with JAX
Experience with debugging, profiling, and implementing software engineering best practices in large-scale systems
Expertise with PyTorch, JIT compilation, and AOT tracing
Experience with CUDA kernels or equivalent ML/low-level kernels
Experience with performant kernel development (e.g., CUTLASS, FlashInfer)
Experience with inference serving platforms (vLLM, SGLang, TensorRT) in production environments
Deep understanding of computer architecture, operating systems, and parallel computing
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.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $129,300/year in our lowest geographic market up to $223,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
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