OPT Machine Learning Engineer Jobs
Machine Learning Engineer roles are among the most OPT-friendly positions in tech, with high demand from employers already familiar with F-1 work authorization. Most roles qualify as STEM OPT, giving you up to 36 months of work authorization, and many employers actively file H-1B visa petitions for strong ML candidates.
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Do you want to join an innovative team of scientists who develop Agentic AI, LLM, and deep learning based solutions to help Amazon provide the best seller experience across the entire Seller life cycle, including recruitment, growth, support, risk mitigation and provide the best customer and seller experience?
Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of customer interactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Are you excited by the opportunity to leverage GenAI and innovate on top of the state-of-the-art large language models to improve customer and seller experience?
Do you like to build end-to-end business solutions and directly impact the profitability of the company? Do you like to innovate and create solutions that have cross-organizational impacts?
If yes, then you may be a great fit to join the Machine Learning Accelerator team.
Key job responsibilities
The scope of an Applied Scientist in the Machine Learning Accelerator (MLA) team is to research and prototype AI and Machine Learning applications that solve strategic business problems across Selling Partner Experience (SPX) domains. Additionally, the scientist collaborates with engineers and business partners to design and implement solutions at scale that are of broad benefit to SPX organizations. They develop large-scale solutions for high impact projects, introduce tools and other techniques that can be used to solve problems from various perspectives, and show depth and competence in more than one area. They influence the team’s technical strategy by making insightful contributions to the team’s priorities, approach and planning. They develop and introduce tools and practices that streamline the work of the team, and they mentor junior team members and participate in hiring.
BASIC QUALIFICATIONS
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- 3+ years of building machine learning models or developing algorithms for business application experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
PREFERRED QUALIFICATIONS
- Experience using Unix/Linux
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
- Experience with LLM fine-tuning, in-context learning, or model evaluation
- Hands-on experience designing reward models or RL post-training pipelines (PPO/GRPO, DPO) for LLMs or agents, including preference-data collection and evaluation
- Experience building agentic AI systems — tool use, planning, retrieval-augmented generation, or multi-agent workflows
- Experience with researching and developing neuro-symbolic solutions and applications
- Publications at top ML/AI venues
- Experience partnering with product/engineering teams to deliver ML in large-scale production system
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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, CA, San Diego
COMPENSATION
- Salary Range: $142,800.00 - $193,200.00 USD annually
DESCRIPTION
Do you want to join an innovative team of scientists who develop Agentic AI, LLM, and deep learning based solutions to help Amazon provide the best seller experience across the entire Seller life cycle, including recruitment, growth, support, risk mitigation and provide the best customer and seller experience?
Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of customer interactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Are you excited by the opportunity to leverage GenAI and innovate on top of the state-of-the-art large language models to improve customer and seller experience?
Do you like to build end-to-end business solutions and directly impact the profitability of the company? Do you like to innovate and create solutions that have cross-organizational impacts?
If yes, then you may be a great fit to join the Machine Learning Accelerator team.
Key job responsibilities
The scope of an Applied Scientist in the Machine Learning Accelerator (MLA) team is to research and prototype AI and Machine Learning applications that solve strategic business problems across Selling Partner Experience (SPX) domains. Additionally, the scientist collaborates with engineers and business partners to design and implement solutions at scale that are of broad benefit to SPX organizations. They develop large-scale solutions for high impact projects, introduce tools and other techniques that can be used to solve problems from various perspectives, and show depth and competence in more than one area. They influence the team’s technical strategy by making insightful contributions to the team’s priorities, approach and planning. They develop and introduce tools and practices that streamline the work of the team, and they mentor junior team members and participate in hiring.
BASIC QUALIFICATIONS
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- 3+ years of building machine learning models or developing algorithms for business application experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
PREFERRED QUALIFICATIONS
- Experience using Unix/Linux
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
- Experience with LLM fine-tuning, in-context learning, or model evaluation
- Hands-on experience designing reward models or RL post-training pipelines (PPO/GRPO, DPO) for LLMs or agents, including preference-data collection and evaluation
- Experience building agentic AI systems — tool use, planning, retrieval-augmented generation, or multi-agent workflows
- Experience with researching and developing neuro-symbolic solutions and applications
- Publications at top ML/AI venues
- Experience partnering with product/engineering teams to deliver ML in large-scale production system
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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, CA, San Diego
COMPENSATION
- Salary Range: $142,800.00 - $193,200.00 USD annually
See all 751+ OPT Machine Learning Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new OPT Machine Learning Engineer Jobs.
Get Access To All JobsTips for Finding OPT Sponsorship as a Machine Learning Engineer
Target companies with active H-1B filing histories
Large tech firms and well-funded AI startups file H-1B visa petitions regularly for ML roles. Filtering by employers with consistent sponsorship track records is the most reliable way to avoid wasting applications on companies that won't convert your OPT.
Lead with your research output and production deployments
Employers sponsoring ML engineers want evidence of real impact. Published papers, Kaggle rankings, open-source contributions, and deployed models in production all signal the kind of technical depth that makes sponsorship worth the investment for a hiring manager.
Apply before your OPT start date, not after
Most ML hiring pipelines take six to twelve weeks from application to offer. Starting your search three to four months before your OPT authorization date gives employers enough runway to complete interviews and onboarding before your work authorization begins.
Clarify your STEM OPT extension eligibility upfront
ML Engineering consistently qualifies under STEM OPT CIP codes tied to computer science and data science programs. Confirming your degree program qualifies before interviews prevents late-stage confusion and reassures employers that 36 months of authorization is on the table.
Specialize in a high-demand ML subfield
Employers sponsoring visas need strong justification for the cost. Specializing in LLM fine-tuning, computer vision, or MLOps makes you a clearer candidate for roles that are genuinely hard to fill, which strengthens the business case for sponsorship.
Negotiate H-1B filing into your offer conversation early
Bringing up sponsorship after receiving an offer creates friction. Raising it naturally during late-stage interviews, once the employer is clearly interested, lets you confirm their willingness to file before you invest further time in the process.
Machine Learning Engineer OPT: Frequently Asked Questions
Do Machine Learning Engineer jobs typically qualify for STEM OPT extension?
Yes. Machine Learning Engineering falls under CIP codes tied to computer science, data science, and electrical engineering programs, all of which are on the STEM Designated Degree Program list. If your degree is in one of those fields, you're eligible to apply for the 24-month STEM OPT extension, giving you up to 36 months of total work authorization.
How do I find Machine Learning Engineer jobs where the employer is willing to sponsor OPT and H-1B?
Migrate Mate filters jobs specifically for F-1 OPT students, so the roles listed are from employers familiar with or actively open to sponsorship. Browsing ML Engineer listings on Migrate Mate saves significant time compared to filtering through general job boards where most postings don't address visa sponsorship at all.
Can I work as a Machine Learning Engineer on OPT before my STEM extension is approved?
Yes, as long as your initial 12-month OPT authorization is active and you've submitted your STEM OPT extension application before it expires. USCIS automatically extends your work authorization by up to 180 days while the extension is pending, so there's no gap in your ability to work, provided you filed on time.
What employment relationship counts as valid for STEM OPT in an ML role?
STEM OPT requires a formal employer-employee relationship, meaning your employer must provide supervision, control your work schedule, and report training outcomes through the SEVP portal. Fully independent contracting arrangements do not satisfy this requirement. Most full-time ML Engineering positions at tech companies meet the standard, but contract-to-hire roles should be reviewed with your DSO before accepting.
Do employers need to pay OPT students market rate for Machine Learning Engineer roles?
STEM OPT regulations require that employers pay OPT students the same wages and working conditions offered to similarly situated U.S. workers in the same role and location. For ML Engineering, which commands strong compensation across the industry, this protection matters. If an employer offers terms noticeably below what comparable full-time employees receive, that arrangement likely doesn't comply with STEM OPT requirements.