STEM OPT Machine Learning Research Jobs
Machine Learning Research roles at E-Verify-enrolled employers qualify for the 24-month STEM OPT extension, giving you up to 36 months of F-1 work authorization. Eligible degrees span computer science, statistics, electrical engineering, and related STEM CIP codes. Your employer must file a formal I-983 training plan with your DSO before your extension begins.
Find STEM OPT Machine Learning Research JobsOverview
Showing 5 of 608+ Machine Learning Research jobs
See all 608+ Machine Learning Research Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Research 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 608+ STEM OPT Machine Learning Research Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new STEM OPT Machine Learning Research Jobs.
Get Access To All JobsTips for Finding STEM OPT Authorization in Machine Learning Research
Verify your CIP code before applying
Check that your degree's Classification of Instructional Programs code appears on the USCIS STEM Designated Degree Program List. Computer science, statistics, and applied mathematics codes typically qualify, but interdisciplinary programs sometimes don't. Confirm with your DSO before targeting roles.
Search employers through Migrate Mate first
Filter your Machine Learning Research job search on Migrate Mate to surface employers with verified E-Verify enrollment. This cuts out the manual legwork of cross-checking each company before you apply and saves you from pursuing dead-end opportunities.
Confirm E-Verify enrollment before accepting any offer
Ask your hiring contact directly whether the employer is enrolled in E-Verify and request their E-Verify Company ID number. An employer that can't provide this cannot legally employ STEM OPT students, regardless of how willing they are to support your extension.
Draft your I-983 training plan before your start date
USCIS requires a completed I-983 on file with your DSO before your STEM OPT extension activates. For Machine Learning Research roles, map specific projects and deliverables to learning objectives that align with your STEM degree field. Generic plans get rejected.
Target research labs with existing OPT infrastructure
University-affiliated research centers, national labs, and large tech R&D divisions process STEM OPT extensions regularly and have HR staff familiar with the I-983 requirements. First-time STEM OPT employers often cause delays because they're unfamiliar with their obligations under the training plan.
Use O*NET to frame your role's specialty occupation credentials
Pull the Machine Learning Research occupation profile from O*NET and match your degree field to the listed knowledge and skills requirements. This documentation strengthens your case if your employer or DSO needs to justify how your degree directly relates to the role.
Frequently Asked Questions
Does a Machine Learning Research role qualify for the STEM OPT extension?
Yes, provided your degree falls under a STEM-designated CIP code and the role directly applies skills from that field. Machine Learning Research positions typically satisfy this because they require quantitative reasoning, algorithm development, and statistical modeling rooted in computer science, mathematics, or engineering disciplines. Confirm your specific degree's eligibility with your DSO using the USCIS STEM Designated Degree Program List.
What E-Verify obligation does my employer have for my STEM OPT?
Your employer must be actively enrolled in E-Verify at the time your STEM OPT extension begins. Enrollment alone isn't sufficient if it lapsed. You can ask your employer to show you their E-Verify Company ID or employer participation page. If they're not enrolled, they cannot legally employ you under a STEM OPT extension, and you'd need to find an enrolled employer before your initial OPT period ends.
What goes into the I-983 training plan for a research role?
The I-983 requires a description of the research objectives, the specific skills you'll develop, how those skills connect to your STEM degree, and measurable goals your employer will evaluate. For Machine Learning Research, this typically means documenting model development cycles, experimental design responsibilities, and technical mentorship structures. Both you and a company supervisor must sign it, and your DSO must receive it before your extension is approved.
How does cap-gap protection work if I'm in a Machine Learning Research role during H-1B season?
If your employer files an H-1B visa petition on your behalf before your OPT expires and you're selected in the lottery, cap-gap automatically extends your work authorization through September 30 of that year. You can continue working in your Machine Learning Research role without interruption during this period. USCIS issues no separate document for cap-gap, your I-20 noting the cap-gap extension serves as proof of authorized status.
Where can I find Machine Learning Research jobs at E-Verify employers?
Migrate Mate filters job listings specifically for employers enrolled in E-Verify, so you're not wasting applications on companies that can't support your STEM OPT extension. Machine Learning Research roles at qualifying employers span university research divisions, national laboratories, and technology companies with active R&D programs. Searching through Migrate Mate surfaces only employers who meet the E-Verify requirement before you apply.