STEM OPT Sr Staff Machine Learning Engineer Jobs
Sr Staff Machine Learning Engineer roles sit squarely within STEM OPT eligibility, drawing on degrees in computer science, statistics, and related STEM fields. Your 24-month extension requires an E-Verify enrolled employer and a signed I-983 training plan tied directly to your ML engineering work.
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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 606+ STEM OPT Sr Staff Machine Learning Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new STEM OPT Sr Staff Machine Learning Engineer Jobs.
Get Access To All JobsTips for Finding STEM OPT Authorization as a Sr Staff Machine Learning Engineer
Verify your CIP code before applying
Pull your degree's Classification of Instructional Programs code from your transcripts and confirm it maps to an approved STEM field on the official STEM Designated Degree Program List. Computer science, applied mathematics, and statistics are all qualifying fields for Sr Staff ML Engineer roles.
Check E-Verify enrollment before every interview
Any company that hires you for STEM OPT must be actively enrolled in E-Verify, not just registered. Confirm enrollment status directly through the E-Verify employer search before you invest time in their interview process.
Build a training plan around ML engineering deliverables
Your I-983 must tie every training goal to actual job duties, so draft it before your start date with specifics: model deployment pipelines, distributed training infrastructure, or MLOps tooling. Vague plans draw DSO scrutiny and slow your extension approval.
Target companies with senior IC engineering tracks
Sr Staff roles sit above Staff but below Principal or Distinguished Engineer, so prioritize employers whose job ladders include that tier explicitly. Companies with mature ML infrastructure, such as those running large-scale model serving or autonomous systems, are most likely to post at this seniority.
Use Migrate Mate to filter for E-Verify employers hiring at this level
Search Sr Staff Machine Learning Engineer listings on Migrate Mate, which surfaces only employers verified to support STEM OPT. Filtering by seniority and E-Verify status upfront cuts the time you waste pursuing roles that can't legally employ you.
Negotiate your offer timeline around your OPT end date
If your initial 12-month OPT expires before your extension is approved, the cap-gap rule protects H-1B visa registrants, but STEM OPT extensions don't work the same way. File your I-765 extension at least 90 days before your EAD expires and confirm the offer start date aligns with your authorized period.
Frequently Asked Questions
Does my degree qualify me for a STEM OPT extension in a Sr Staff Machine Learning Engineer role?
Your degree qualifies if it carries a STEM-designated CIP code, which covers computer science, electrical engineering, applied mathematics, statistics, and several adjacent fields. The role itself must also relate directly to your degree field. Confirm your CIP code appears on the official STEM Designated Degree Program List maintained by the Department of Homeland Security, and verify the connection with your DSO before applying.
Does the employer offering me a Sr Staff Machine Learning Engineer position need to be E-Verify enrolled?
Yes, E-Verify enrollment is a hard requirement for STEM OPT, not optional. Before accepting any offer, use the E-Verify employer search to confirm the specific legal entity, not just the parent company, is actively enrolled. A subsidiary or staffing agency operating under a different EIN may not share the parent's enrollment, which would invalidate your STEM OPT authorization.
What goes into the I-983 training plan for a Sr Staff ML Engineer role?
The I-983 must describe how your training goals connect to your STEM degree and to the specific technical work you'll perform. For a Sr Staff Machine Learning Engineer, that means listing concrete deliverables: designing model training pipelines, architecting inference systems, or leading research-to-production workflows. Generic descriptions like 'machine learning work' are insufficient. Your employer's authorized representative and your DSO must both sign the plan before your extension is filed.
How do I find Sr Staff Machine Learning Engineer jobs where employers already understand STEM OPT requirements?
Migrate Mate lists Sr Staff Machine Learning Engineer openings filtered to E-Verify enrolled employers, so you're only seeing roles where the legal groundwork is already in place. At the Sr Staff level, hiring managers in ML-intensive organizations tend to be more familiar with STEM OPT than early-career teams, but confirming E-Verify status yourself before the offer stage remains essential regardless of employer size.
What happens to my STEM OPT authorization if my extension is still pending when my current EAD expires?
If you filed a timely STEM OPT extension, meaning at least 90 days before your EAD expiration, USCIS grants a 180-day automatic extension of your work authorization while the application is pending. You can continue working for your E-Verify employer during that window. Your employer must re-verify your employment authorization in E-Verify once your new EAD arrives.