STEM OPT ML Software Engineer Jobs
ML Software Engineer roles sit squarely within STEM OPT eligibility, letting you work for up to 36 months total on your F-1 authorization. Your employer must be enrolled in E-Verify to hire you on the 24-month extension. A degree in computer science, electrical engineering, or a related STEM field qualifies.
Find STEM OPT ML Software Engineer JobsOverview
Showing 5 of 598+ ML Software Engineer jobs
See all 598+ ML Software Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new ML Software Engineer roles.
Get Access To All JobsDESCRIPTION
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 598+ STEM OPT ML Software Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new STEM OPT ML Software Engineer Jobs.
Get Access To All JobsTips for Finding STEM OPT Authorization as a ML Software Engineer
Verify your CIP code before applying
Your degree's Classification of Instructional Programs code must fall under an approved STEM field. Cross-check it against the DHS STEM Designated Degree Program List before you target roles, so you don't discover an eligibility gap after an offer.
Confirm E-Verify enrollment before accepting offers
Ask recruiters directly whether the company is enrolled in E-Verify, not just whether they 'sponsor' OPT. An employer can sponsor your initial OPT but still be ineligible for the 24-month STEM extension if they haven't completed E-Verify enrollment.
Use Migrate Mate to target verified STEM OPT employers
Search ML Software Engineer roles on Migrate Mate, which surfaces employers with confirmed E-Verify enrollment. This cuts the time you'd spend manually vetting companies and lets you focus applications on roles you're actually authorized to take.
Prepare your I-983 training plan before day one
Your employer must sign a Form I-983 outlining your training goals, duties, and learning objectives before your STEM OPT extension begins. Draft the ML-specific learning milestones in advance so the hiring manager isn't surprised by the paperwork requirement.
Target research-intensive ML roles for stronger I-983 alignment
Roles involving model development, research engineering, or applied ML research map more cleanly to the I-983's requirement that training relate directly to your STEM degree. Purely ops-focused or data-labeling roles can draw DSO scrutiny during reporting cycles.
File the STEM OPT extension at least 90 days early
USCIS recommends filing your I-765 extension application 90 days before your current EAD expires. ML hiring cycles can run long, so coordinating your offer timeline with your filing window prevents gaps in authorization between positions.
Frequently Asked Questions
Does my degree qualify me for the STEM OPT extension as an ML Software Engineer?
Your degree qualifies if it appears on the DHS STEM Designated Degree Program List, which covers fields like computer science, electrical engineering, mathematics, and statistics. Most ML Software Engineer roles require exactly these backgrounds. Confirm your specific Classification of Instructional Programs code with your DSO before filing, since the CIP code on your transcript determines eligibility, not just the degree name.
Does every employer hiring ML Software Engineers on STEM OPT need to be in E-Verify?
Yes, E-Verify enrollment is a hard requirement for the 24-month STEM OPT extension, not optional. Your employer must be enrolled before your extension is approved, and they must remain enrolled throughout your authorization period. If a company withdraws from E-Verify while you're employed, your STEM OPT authorization is at risk. Always verify enrollment status before accepting an offer. You can find ML Software Engineer roles from confirmed E-Verify employers on Migrate Mate.
What goes into the I-983 training plan for an ML Software Engineer role?
The I-983 requires your employer to describe the specific training you'll receive, how your duties relate to your STEM degree, and measurable learning objectives. For ML Software Engineer positions, this typically covers model architecture development, applied research methods, or ML systems engineering tied to your coursework. Both you and a company supervisor must sign it, and your DSO must receive it before your extension period begins.
Can I change ML Software Engineer jobs while on the STEM OPT extension?
Yes, you can change employers, but the new employer must also be enrolled in E-Verify, and you must report the change to your DSO within 10 days. A new I-983 training plan must be completed with the new employer before you start. Your existing EAD remains valid, but working for a non-E-Verify employer, even briefly, violates your status. Plan job transitions carefully around your authorization end date.
How does cap-gap protection work if I'm an ML Software Engineer with a pending H-1B?
If your employer files an H-1B visa petition on your behalf before your OPT or STEM OPT expires and you're selected in the lottery, cap-gap automatically extends your work authorization through September 30 of that fiscal year. This means you can continue working in your ML Software Engineer role without interruption while the H-1B is pending. USCIS governs cap-gap rules, and your DSO can issue an updated I-20 reflecting the extension.