STEM OPT AI ML Engineering Jobs
AI ML Engineering roles in computer science, data science, and related STEM fields qualify for the 24-month STEM OPT extension, giving you up to 36 months of total work authorization. Your employer must be enrolled in E-Verify to file your I-983 training plan and keep your authorization active.
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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 600+ STEM OPT AI ML Engineering Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new STEM OPT AI ML Engineering Jobs.
Get Access To All JobsTips for Finding STEM OPT Authorization in AI ML Engineering
Verify your CIP code before applying
Your STEM OPT eligibility depends on your degree's Classification of Instructional Programs code matching an approved STEM list. Computer science, electrical engineering, and data science CIP codes commonly support AI ML roles, but confirm with your DSO before targeting employers.
Filter employers by E-Verify enrollment status
Only E-Verify-enrolled employers can legally hire you on STEM OPT. Before any application, confirm enrollment through the E-Verify employer search tool. Roles at non-enrolled companies, including many early-stage startups, are off-limits regardless of how strong the offer looks.
Build an I-983 training plan before the offer stage
Drafting your training plan goals for an AI ML role before you receive an offer lets you move faster once one comes. Map your learning objectives to specific ML frameworks, model development responsibilities, and performance benchmarks your employer will sign off on.
Target employers with active H-1B filing history
Companies that have consistently filed H-1B visa petitions for ML engineers are structurally prepared to support long-term authorization. Use Migrate Mate to filter AI ML Engineering roles by employers with verified sponsorship history, so you're not starting that conversation from scratch.
Align your role title with DOL wage classifications
Job titles in AI and ML vary widely, but DOL wage levels are tied to SOC codes like Software Developers or Computer and Information Research Scientists. Use the OFLC Wage Search to confirm which SOC code your offer maps to before negotiating, since misclassification can delay LCA certification.
Track your 24-month extension window against H-1B cap dates
If your STEM OPT expires before an H-1B petition takes effect, cap-gap protection may bridge the gap, but only if your employer files before April 1 of the relevant fiscal year. Coordinate your extension end date with your employer's HR team early so filing deadlines don't catch you off guard.
Frequently Asked Questions
Which STEM degrees qualify for the OPT extension for AI ML Engineering roles?
Degrees in computer science, electrical engineering, statistics, applied mathematics, and data science are among the most common qualifying fields for AI ML Engineering positions. Your degree must carry an approved STEM Classification of Instructional Programs code, and your DSO can confirm whether your specific program qualifies before you begin the extension application through USCIS.
Does my employer have to be enrolled in E-Verify to hire me on STEM OPT?
Yes. E-Verify enrollment is a hard requirement for every employer hiring STEM OPT students. There are no exceptions, even for short contracts or part-time roles. You can verify a company's enrollment status through the E-Verify employer search before accepting any offer. Working for a non-enrolled employer places your immigration status at risk.
What goes into the I-983 training plan for an AI ML Engineering position?
Your I-983 must describe the specific AI and ML skills you'll develop, the projects or responsibilities tied to those goals, how your work connects to your STEM degree, and how your employer will evaluate your progress. For AI ML Engineering roles, this typically includes model development, data pipeline work, framework proficiency, and measurable performance benchmarks signed off by a supervisor.
How does cap-gap protection work if my STEM OPT ends before my H-1B starts?
If your employer files an H-1B petition on your behalf before April 1 and your STEM OPT expires between April 1 and October 1 of the same year, cap-gap automatically extends your work authorization through September 30. Your employer must file before that deadline for protection to apply. USCIS provides guidance on cap-gap rules for F-1 students transitioning to H-1B status.
Where can I find AI ML Engineering jobs at employers already set up for STEM OPT students?
Migrate Mate lists AI ML Engineering roles filtered by employers with active E-Verify enrollment and a track record of sponsoring STEM workers. Searching there lets you focus on companies that already understand the I-983 process and are structurally prepared to support your authorization, rather than spending time educating employers who have never hired an OPT student.