Software Engineer- AI/ML, Amazon Neuron Training

Amazon Web Services (AWS)

Cupertino (CA)

On-site

USD 165,000 - 224,000

Full time

2 days ago
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Job summary

Annapurna Labs at AWS (Amazon Web Services) builds AWS Neuron to accelerate deep learning and GenAI workloads on Trainium. The team works across ML compiler, runtime, collectives, and framework integration with PyTorch/JAX to train frontier models.

You will lead efforts to optimize distributed training throughput on Trainium, collaborating with PyTorch and Neuron teams to enable large-scale training. The role blends ML, high-performance computing, and distributed systems in a fast-paced

Qualifications

  • 3+ years of non-internship software development experience.
  • 2+ years of design or architecture (patterns, reliability, scaling) experience.
  • Experience programming with at least one software programming language.

Responsibilities

  • Lead optimization of distributed training performance on Trainium across the Neuron stack.
  • Own parallelism strategies spanning data, tensor, pipeline, expert, and context parallelism.

Skills

Software development experience (non‑h
Design/architecture experience
Programming languages

Education

Bachelor's degree in computer science or equivalent

Job description

Description

The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on AWS Trainium, Amazon's custom machine learning accelerator. Neuron includes an ML compiler, runtime, collectives library, and application framework that integrate with PyTorch and JAX, so customers can train frontier-scale models on Trainium without rewriting their stack.


Key job responsibilities

The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on AWS Trainium, Amazon's custom machine learning accelerator. Neuron includes an ML compiler, runtime, collectives library, and application framework that integrate with PyTorch and JAX, so customers can train frontier-scale models on Trainium without rewriting their stack.


As part of the broader Neuron organization, our team works across multiple technology layers, from frameworks and kernels through to the compiler, runtime, and collectives teams. This is hardware and software co-design in practice. A single throughput gap rarely sits in one layer, so tracing it means following the problem across the stack, deciding where the fix belongs, and working with the team that owns that layer to land it. We not only optimize current performance but also contribute to future architecture designs, since the gaps we characterize today become requirements for the next generation of Trainium. We work closely with customers to enable their models and ensure they train efficiently. This role offers a rare opportunity to work at the intersection of machine learning, high-performance computing, and distributed systems, where you will help shape the direction of AI acceleration technology.


Your role will help lead the effort to optimize distributed training performance on Trainium, with a primary focus on training throughput across the Neuron software stack. You will work across PyTorch and Neuron solftware stack with amazing Neuron compiler and runtime teams to enable and tune large-scale training workloads on the latest Trainium instances. You will own the parallelism strategies these models depend on, spanning data, tensor, pipeline, expert, and context parallelism, and apply reduced-precision formats where they measurably pay off. You will profile end to end to determine whether a workload is bound by compute, memory, collectives, or host overhead, then drive the fix to the layer that owns it, working with compiler, runtime, and collectives engineers to land it. You will translate the performance gaps you characterize into requirements that influence frameworks, and contribute upstream to the open source frameworks our customers train on.


About The Team

About Us

Inclusive Team Culture

Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.


Work/Life Balance

Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.


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

  • Experience programming with at least one software programming language


Preferred Qualifications


  • 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


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


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, CA, Cupertino - 165,200.00 - 223,600.00 USD annually


USA, WA, Seattle - 143,700.00 - 194,400.00 USD annually


Company - Annapurna Labs (U.S.) Inc.

Job ID: A10501454

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