ML Compiler Engineer, Neuron Kernel Interface

Amazon is hiring an ML Compiler Engineer, Neuron Kernel Interface in Seattle, United States. It pays $165k-$224k a year and Level rates it ; you can apply on Level.

Level's read on this job

Level rates this role Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include PyTorch, Jax and Trainium.
Level files it as a mid-level role.
Level lists 22,000+ other open roles at Amazon.
The role was posted in the last few days.

AI in this role

Develop and scale machine learning compilers and kernel interfaces for AWS Trainium custom AI accelerators.

pytorchjaxtrainiumneuron-sdk

compiler-designmachine-learningkernel-optimizationc

AWS Machine Learning accelerators are at the forefront of AWS innovation. Trainium delivers best-in-class ML training performance with the most teraflops (TFLOPS) of compute power for ML in the cloud. This is all enabled by the AWS Neuron Software Development Kit (SDK), which includes an ML compiler, the Neuron Kernel Interface (NKI) compiler, and a runtime that natively integrates into popular ML frameworks such as PyTorch and JAX.

Neuron Kernel Interface (NKI) is a bare-metal language and compiler for directly programming AWS Trainium instances. You can use NKI to develop, optimize, and run new operators directly on hardware while making full use of available compute and memory resources.

Explore NKI:
- https://awsdocs-neuron.readthedocs-hosted.com/en/latest/nki/index.html

AWS Neuron is used at scale by customers such as Epic Games, Snap, Airbnb, Autodesk, Amazon Alexa, and Amazon Rekognition, along with many others across a range of segments.

Amazon's Annapurna Labs is responsible for building innovative silicon and software for AWS customers. We are at the forefront of innovation, combining cloud scale with the world's most talented engineers. Our team covers multiple disciplines including silicon engineering, hardware design and verification, software, and operations. With such breadth of talent, there is opportunity to learn all the time. We operate in spaces that are very large, yet our teams remain small and agile. There is no blueprint. We're inventing. We're experimenting. When you couple that with the ability to work on so many different products and services, it makes for a unique learning culture.

Learn more about our history:
- https://www.amazon.science/how-silicon-innovation-became-the-secret-sauce-behind-awss-success

Explore the Product:
- https://aws.amazon.com/machine-learning/neuron/

As an ML Compiler Engineer with the Annapurna Neuron team, you develop and scale compilers to handle the world's largest ML workloads on AWS Trainium. You design and ship features across the compiler stack, work directly with Amazon ML partner teams to unblock their workloads, and grow your skills alongside senior engineers. You want to work close to the hardware, help bring new products and features to market, and ship high-quality code.

A background in Machine Learning, AI accelerators, or Kernel performance is helpful, but not required.

In order to be considered for this role, candidates must be located near or willing to relocate to Seattle or Cupertino.

Key job responsibilities
- Design and build compiler features for the Neuron compilers. Your work may span the frontend, intermediate representations, optimization passes, and code generation for the hardware.
- Own features from design through production: design, implement, test on hardware and simulation, and support customers.
- Work with Amazon ML teams (PyTorch, JAX, and internal model teams) to understand their workloads and unblock them.
- Give hardware teams compiler and programmability feedback on upcoming chips.
- Review code and designs, and mentor newer engineers.

A day in the life
You start the day reviewing a teammate's code for a new compiler feature, leaving feedback before it merges. Mid-morning, you work with a model team whose kernel isn't reaching peak performance on Trainium, and you trace the bottleneck through the compiler's passes. After lunch, you write and test the feature you designed last week, then run it on hardware to confirm correctness. The team moves quickly and you are always in the details.

About the team
We are the compiler teams behind AWS Neuron, the software that runs machine learning models on Amazon Trainium. Our ML compiler takes a full model from PyTorch or JAX and compiles the graph to run across the chip. The Neuron Kernel Interface (NKI) is a bare-metal language and compiler that lets customers program Trainium directly, down to the compute and memory, for a new operator or a faster kernel. Together we own the path from framework model to silicon. Our mission is to make Trainium fast and fully programmable.

Our teams are small and agile, and there is opportunity to learn all the time. We work closely with the hardware, runtime and framework teams, and with the Amazon ML teams who use our compilers.

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

- 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

How we rate this

ML Compiler Engineer, Neuron Kernel Interface at Amazon rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

ML Compiler Engineer, Neuron Kernel Interface is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $165k-$224k a year.
The role is based in US, WA, Seattle.
The job was posted on October 17, 2026.
Seniority: mid.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Compiler DesignMachine learningKernel OptimizationCPyTorchJaxTrainiumNeuron Sdk

Questions you could be asked

Tell me about a project where compiler design was part of your work. What did you do?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where kernel optimization was part of your work. What did you do?
Tell me about a project where c was part of your work. What did you do?
Walk me through how you've used PyTorch in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Compiler Design, Machine learning, Kernel Optimization, C, and PyTorch. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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