STEM OPT ML Engineer Jobs
ML Engineer roles qualify for STEM OPT because they fall under computer science and engineering CIP codes, giving you up to 24 months of additional work authorization beyond your initial OPT period. Your employer must be enrolled in E-Verify, and you'll need an approved I-983 training plan tied to a qualifying STEM degree.
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Annapurna Labs was a startup company acquired by AWS in 2015, and is now fully integrated. If AWS is an infrastructure company, then think Annapurna Labs as the infrastructure provider of AWS. Our org covers multiple disciplines including silicon engineering, hardware design and verification, software, and operations. AWS Nitro, ENA, EFA, Graviton and F1 EC2 Instances, AWS Neuron, Inferentia and Trainium ML Accelerators, and in storage with scalable NVMe, are some of the products we have delivered, over the last few years.
AWS Neuron is the complete software stack for the AWS Inferentia and Trainium cloud-scale machine learning accelerators and the Trn1 and Inf1 servers that use them. This position is for a Software Engineer that will lead the development of machine learning tools to run, optimize, and analyze machine learning workloads. This candidate must have had experience leading machine learning tool projects, preferably starting from architecture through several generations of delivery to customers. Deep knowledge of profiling and optimization, resource management, scheduling, code generation are needed. The ideal candidate will have worked on new instruction set architectures, which may include CPU, NPU, GPU and other forms of compute.
Key job responsibilities
This engineer will lead the design and implementation of ML infrastructure platform, building systems for capacity management, workload scheduling, and fleet orchestration across ML accelerators. They will work with ML scientists, training infrastructure engineers, hardware teams, and internal customers to ensure the ML Infra service delivers seamless ML Accelerator access with low wait times, high utilization, and zero-config deployment from various environments.
A day in the life
As you design and code solutions to help our team drive efficiencies in software architecture, you’ll create metrics, implement automation and other improvements, and resolve the root cause of software defects. You’ll also:
- Build high-impact solutions to deliver to our large customer base.
- Participate in design discussions, code review, and communicate with internal and external stakeholders.
- Work cross-functionally to help drive business decisions with your technical input.
- Work in a startup-like development environment, where you’re always working on the most important stuff.
About the team
- High-impact, high-visibility: You'll directly accelerate every Neuron team's ability to ship — your work multiplies the output of 100+ engineers
- Greenfield opportunities: We're actively building new capabilities with significant design ownership for SDEs
- Small, senior team: where every person owns major components and drives architectural decisions
- AI infrastructure: Work at the intersection of Kubernetes, custom silicon, and large-scale ML workloads
Diverse Experiences
We value diverse experiences and non-traditional career paths. If your career is just starting or includes alternative experiences, we encourage you to apply.
Inclusive Team Culture
Our employee-led affinity groups foster inclusion. Events like CORE and AmazeCon inspire us to embrace our uniqueness.
Work/Life Balance
We strive for flexibility as part of our working culture, supporting you both at work and at home.
Mentorship & Career Growth
We offer knowledge-sharing, mentorship, and one-on-one code reviews to help you grow as a professional.
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
- 2+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization or search experience
- Strong proficiency in Go/Java, Python and working knowledge Javascript/TypeScript
- Experience building and operating large-scale distributed systems on Kubernetes
- Experience designing, deploying, and maintaining production services at scale, including on-call ownership
- Experience with machine learning infrastructure — orchestration, scheduling, or resource management at scale
- Proficiency in application and kernel-level performance profiling and optimization
- Experience with integrated software/hardware performance analysis in heterogeneous compute environments
- Proficiency in observability and telemetry — instrumentation, metrics collection, alarming, dashboarding, and monitoring
- Experience debugging complex issues in large-scale distributed systems and driving best practices
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.
LOCATION
USA, WA, Seattle - 143,700.00 - 194,400.00 USD annually
DESCRIPTION
Annapurna Labs was a startup company acquired by AWS in 2015, and is now fully integrated. If AWS is an infrastructure company, then think Annapurna Labs as the infrastructure provider of AWS. Our org covers multiple disciplines including silicon engineering, hardware design and verification, software, and operations. AWS Nitro, ENA, EFA, Graviton and F1 EC2 Instances, AWS Neuron, Inferentia and Trainium ML Accelerators, and in storage with scalable NVMe, are some of the products we have delivered, over the last few years.
AWS Neuron is the complete software stack for the AWS Inferentia and Trainium cloud-scale machine learning accelerators and the Trn1 and Inf1 servers that use them. This position is for a Software Engineer that will lead the development of machine learning tools to run, optimize, and analyze machine learning workloads. This candidate must have had experience leading machine learning tool projects, preferably starting from architecture through several generations of delivery to customers. Deep knowledge of profiling and optimization, resource management, scheduling, code generation are needed. The ideal candidate will have worked on new instruction set architectures, which may include CPU, NPU, GPU and other forms of compute.
Key job responsibilities
This engineer will lead the design and implementation of ML infrastructure platform, building systems for capacity management, workload scheduling, and fleet orchestration across ML accelerators. They will work with ML scientists, training infrastructure engineers, hardware teams, and internal customers to ensure the ML Infra service delivers seamless ML Accelerator access with low wait times, high utilization, and zero-config deployment from various environments.
A day in the life
As you design and code solutions to help our team drive efficiencies in software architecture, you’ll create metrics, implement automation and other improvements, and resolve the root cause of software defects. You’ll also:
- Build high-impact solutions to deliver to our large customer base.
- Participate in design discussions, code review, and communicate with internal and external stakeholders.
- Work cross-functionally to help drive business decisions with your technical input.
- Work in a startup-like development environment, where you’re always working on the most important stuff.
About the team
- High-impact, high-visibility: You'll directly accelerate every Neuron team's ability to ship — your work multiplies the output of 100+ engineers
- Greenfield opportunities: We're actively building new capabilities with significant design ownership for SDEs
- Small, senior team: where every person owns major components and drives architectural decisions
- AI infrastructure: Work at the intersection of Kubernetes, custom silicon, and large-scale ML workloads
Diverse Experiences
We value diverse experiences and non-traditional career paths. If your career is just starting or includes alternative experiences, we encourage you to apply.
Inclusive Team Culture
Our employee-led affinity groups foster inclusion. Events like CORE and AmazeCon inspire us to embrace our uniqueness.
Work/Life Balance
We strive for flexibility as part of our working culture, supporting you both at work and at home.
Mentorship & Career Growth
We offer knowledge-sharing, mentorship, and one-on-one code reviews to help you grow as a professional.
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
- 2+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization or search experience
- Strong proficiency in Go/Java, Python and working knowledge Javascript/TypeScript
- Experience building and operating large-scale distributed systems on Kubernetes
- Experience designing, deploying, and maintaining production services at scale, including on-call ownership
- Experience with machine learning infrastructure — orchestration, scheduling, or resource management at scale
- Proficiency in application and kernel-level performance profiling and optimization
- Experience with integrated software/hardware performance analysis in heterogeneous compute environments
- Proficiency in observability and telemetry — instrumentation, metrics collection, alarming, dashboarding, and monitoring
- Experience debugging complex issues in large-scale distributed systems and driving best practices
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.
LOCATION
USA, WA, Seattle - 143,700.00 - 194,400.00 USD annually
See all 621+ STEM OPT ML Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new STEM OPT ML Engineer Jobs.
Get Access To All JobsTips for Finding STEM OPT Authorization as a ML Engineer
Verify your CIP code matches ML Engineering
Check your degree's Classification of Instructional Programs code against the DHS STEM OPT designated degree list before applying. Computer Science (11.0701), Electrical Engineering (14.1001), and Applied Mathematics (27.0301) are common qualifying codes for ML Engineer roles.
Confirm E-Verify enrollment before accepting offers
Ask recruiters for their E-Verify company ID or check enrollment status directly through the E-Verify employer search tool. An employer not enrolled in E-Verify cannot legally support your STEM OPT extension, regardless of how eager they are to hire you.
Use Migrate Mate to filter ML Engineer roles by E-Verify status
Search ML Engineer positions on Migrate Mate to surface employers already verified for STEM OPT eligibility. This cuts the research time of manually cross-referencing job postings against E-Verify enrollment records before you invest in an application.
Build your I-983 training plan around ML deliverables
Draft your I-983 before your offer letter is finalized so your hiring manager can sign off quickly. Map specific ML Engineering tasks, such as model training pipelines and production deployment, to your STEM degree's learning objectives to satisfy USCIS review standards.
Target employers with active H-1B filing history in ML roles
Companies that regularly file H-1B visa petitions for software and ML roles have established immigration infrastructure and understand STEM OPT reporting obligations. DOL LCA disclosure data shows which employers file for ML Engineer-adjacent SOC codes year over year.
Time your STEM OPT application to cover your start date
File your STEM OPT extension with your DSO at least 90 days before your initial OPT expires. USCIS recommends submitting Form I-765 early enough that your EAD arrives before your authorization lapses, protecting your ability to start on your target date.
Frequently Asked Questions
Does an ML Engineer role qualify for the STEM OPT extension?
ML Engineer positions typically qualify when your employer maps the role to a STEM-designated SOC code, such as Software Developers (15-1252) or Computer and Information Research Scientists (15-1221), and your degree falls under a qualifying CIP code. Confirm the match with your DSO before filing. You can verify the SOC classification for ML Engineering work through O*NET.
What STEM degrees are accepted for an ML Engineer STEM OPT extension?
Degrees in Computer Science, Electrical Engineering, Applied Mathematics, Statistics, and Data Science are commonly accepted, provided they appear on the DHS STEM Designated Degree Program list under their respective CIP codes. A degree in a non-STEM field does not qualify even if your coursework included machine learning. Your DSO can confirm your specific CIP code eligibility before you apply.
How do I verify that an ML Engineer employer is enrolled in E-Verify?
Use the E-Verify employer search tool to look up any company by name before accepting an offer. Enrollment in E-Verify is a legal requirement for STEM OPT employers, not an optional benefit. If a company is not enrolled, they cannot legally employ you under the STEM OPT extension. Migrate Mate surfaces ML Engineer roles from E-Verify-enrolled employers so you can focus your search efficiently.
What goes into an I-983 training plan for an ML Engineer position?
Your I-983 must describe how the ML Engineer role provides practical training related to your STEM degree. Include specific responsibilities such as developing neural network architectures, running model validation pipelines, or deploying inference systems, and explain how each connects to your academic coursework. Both you and your employer's authorized representative must sign it, and your DSO must review and maintain it throughout your extension period.
Does cap-gap protection apply if my H-1B is selected while I work as an ML Engineer on STEM OPT?
Yes. If your employer files an H-1B petition on your behalf before your STEM OPT EAD expires, cap-gap protection automatically extends your work authorization through September 30 of that fiscal year, or until your H-1B start date of October 1, whichever comes first. You can continue working as an ML Engineer without interruption as long as the petition remains pending or approved. USCIS provides formal guidance on cap-gap eligibility on its website.