STEM OPT Machine Learning Jobs
Machine Learning roles in data science, NLP, and computer vision fall under STEM-designated CIP codes, making them eligible for the 24-month STEM OPT extension beyond your initial 12 months. Your employer must be enrolled in E-Verify and sign an I-983 training plan before your DSO can authorize the extension.
Find STEM OPT Machine Learning JobsOverview
Showing 5 of 621+ Machine Learning jobs
See all 621+ Machine Learning Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning 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 621+ STEM OPT Machine Learning Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new STEM OPT Machine Learning Jobs.
Get Access To All JobsTips for Finding STEM OPT Authorization in Machine Learning
Verify your degree's CIP code eligibility
Check that your degree program's CIP code appears on the STEM Designated Degree Program List published by USCIS. Computer Science (11.07), Electrical Engineering (14.10), and Statistics (27.05) all qualify, but not every data-adjacent program does.
Confirm E-Verify enrollment before applying
Machine Learning roles at startups and research labs often lack E-Verify enrollment, which disqualifies them from STEM OPT. Search the E-Verify employer search tool by company name before submitting any application.
Target employers with active ML-specific LCA filings
Use Migrate Mate to filter for employers with verified Labor Condition Application filings under SOC codes like 15-2051 (Data Scientists) and 15-1252 (Software Developers). These companies have already navigated STEM OPT and understand the I-983 obligation.
Negotiate your I-983 training plan before signing
Your offer letter is not enough: USCIS requires a signed I-983 detailing your ML learning objectives, supervision structure, and how the role relates to your degree. Raise this document with HR during the offer stage, not after your start date.
Check prevailing wage against the OFLC Wage Search
Your employer must pay at least the DOL prevailing wage for your occupation and location. Run your job title and county through the OFLC Wage Search to catch underpaid offers early, since a below-wage role can jeopardize future H-1B visa sponsorship.
Build a portfolio aligned with O*NET ML task definitions
O*NET defines Machine Learning Engineers and Data Scientists by specific tasks: model training, feature engineering, and deployment pipelines. Structure your GitHub portfolio and resume around those task definitions so your application maps cleanly to the specialty occupation standard.
Frequently Asked Questions
Which STEM degrees qualify for the STEM OPT extension in Machine Learning roles?
Degrees in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, and Data Science typically qualify, provided their CIP code appears on the STEM Designated Degree Program List published by USCIS. A Machine Learning or AI-specific master's program qualifies if the CIP code is listed. Degrees in Business Analytics or Information Systems may not qualify, so confirm your CIP code with your DSO before targeting STEM OPT positions.
Does every Machine Learning employer need to be enrolled in E-Verify?
Yes, E-Verify enrollment is a hard requirement for STEM OPT. No employer exemptions exist regardless of company size, funding stage, or role seniority. Many early-stage AI startups and university spin-outs are not yet enrolled, so verify enrollment directly through the E-Verify employer search before accepting an offer. Migrate Mate filters for E-Verify-enrolled employers so you can focus on companies that are already eligible.
What goes into the I-983 training plan for a Machine Learning role?
The I-983 must describe your learning objectives in relation to your degree, identify your direct supervisor, outline how the ML work connects to your field of study, and include a schedule for self-evaluations every six months. For Machine Learning roles, training plans typically document objectives around model development, research methodologies, and software engineering practices. Your employer signs the form and your DSO endorses it before USCIS authorizes your EAD extension.
How does cap-gap protection apply if my employer files for H-1B during my STEM OPT period?
If your employer files a timely H-1B petition before your STEM OPT EAD expires and you are selected in the lottery, cap-gap automatically extends your work authorization through September 30 of that fiscal year. You can continue working as a Machine Learning engineer during this period without a new EAD. If your petition is not selected, your STEM OPT authorization continues until its original expiration date, assuming the petition was filed before that date.
How do I find Machine Learning jobs where employers already understand STEM OPT requirements?
Search Migrate Mate for Machine Learning roles filtered by E-Verify-enrolled employers with active LCA filing history under relevant SOC codes. Employers who have previously filed LCAs for data science or software engineering roles are more likely to have internal processes for onboarding STEM OPT students and completing the I-983 training plan without delays.