AI Engineer Jobs in USA with Visa Sponsorship
AI engineers who specialize in taking models from research prototypes to production-grade systems are in surging demand across US companies scaling their AI capabilities. Unlike research-focused roles, AI engineering emphasizes MLOps, model serving, and building reliable inference pipelines - skills that are scarce and highly valued by employers filing sponsorship petitions. If you can bridge the gap between a trained model and a product that serves millions of users, US companies will sponsor your visa to do exactly that. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsPowering Performance Marketplaces in Digital Media
QuinStreet is a pioneer in powering decentralized online marketplaces that match searchers and "research and compare" consumers with brands. We run these virtual- and private-label marketplaces in one of the nation's largest media networks.
Our industry leading segmentation and AI-driven matching technologies help consumers find better solutions and brands faster. They allow brands to target and reach in-market customer prospects with pinpoint segment-by-segment accuracy, and to pay only for performance results.
Our campaign-results-driven matching decision engines and optimization algorithms are built from over 20 years and billions of dollars of online media experience.
We believe in:
- The direct measurability of digital media.
- Performance marketing. (We pioneered it.)
- The advantages of technology.
We bring all this together to deliver truly great results for consumers and brands in the world's biggest channel.
Job Category
We are looking for a Senior AI Developer & Cloud Architect to design, build, and own the AI-powered compliance scraping engine and cloud infrastructure layer for an internal platform monitoring up to 70,000 credit card offer pages per month. This is a hands-on, sole-builder contractor role that sits at the intersection of cloud architecture, AI engineering, and large-scale web scraping, with a clear mandate: deliver a production-grade system that detects compliance violations across issuer offer pages with high accuracy and controlled token costs.
You will do this by architecting the AWS environment from the ground up, building a containerized worker fleet that integrates Playwright rendering with Claude-powered contextual analysis, and defining clean API contracts with the internal team that owns the Laravel control panel.
This is not a managed-PaaS or prototype role. You will be accountable for end-to-end delivery — architecture, build, documentation, and knowledge transfer — owning the full scraping and AI pipeline from URL intake through compliance findings, screenshot evidence, and results delivery back to the portal.
Responsibilities
- Design and configure the production AWS environment (ECS/Fargate, SQS, API Gateway, RDS PostgreSQL, S3, IAM, CloudWatch) using infrastructure as code (Terraform or CDK).
- Build a stateless, containerized worker fleet that integrates Playwright-based page rendering, structured rule evaluation, and Claude API analysis.
- Implement token optimization strategies across the LLM pipeline — prompt engineering, context pruning, caching, model selection, and batching — with measurable cost outcomes.
- Define and document API contracts, job payload schemas, and database write patterns with the internal Laravel portal team to enable parallel development.
- Build third-party API ingestion and field-level diff-detection logic that automatically adjusts monitoring rules when product data changes.
- Handle modern web rendering challenges at scale: JavaScript-heavy SPAs, interstitials, cookie consent overlays, dynamic content, viewport switching, and full-page screenshot capture.
- Evaluate when LLM analysis is the correct tool versus a classifier or rules-based approach, and design the two-stage rule-engine-plus-AI pipeline accordingly.
- Build and maintain a unit test suite covering all modules and APIs to ensure uptime and proper functionality.
- Document every architecture decision, configuration, API contract, and operational procedure continuously — not as a final-week deliverable.
- Deliver a complete runbook and knowledge transfer to the internal team at engagement close.
- Operate independently end-to-end while coordinating closely with the internal portal team and reporting directly to the Senior Director, surfacing risks and trade-offs early.
Requirements
- Production backend Python experience, including async patterns, type hints, packaging, and testing.
- Direct production experience designing and configuring AWS ECS/Fargate, SQS, API Gateway, RDS (PostgreSQL), S3, IAM, and CloudWatch, with infrastructure as code (Terraform or CDK).
- Real shipped systems calling the Anthropic Claude API in production, with demonstrated experience in prompt design, structured output, error handling, and cost trade-offs.
- Demonstrated track record reducing token spend on production LLM workloads, with specific before/after results you can walk through.
- Working knowledge of other LLM providers sufficient to recommend cheaper or better alternatives for specific tasks.
- Production Playwright experience at scale, including headless Chromium failure modes, network idle detection, dynamic content handling, viewport switching, and screenshot strategy. Selenium or Puppeteer experience does not substitute.
- Machine learning fundamentals sufficient to evaluate when LLM analysis is the right tool versus a classifier or rules-based approach, and to reason about evaluation and false-positive rates.
- Docker and containerization experience, including image optimization, ECR, and stateless worker design.
- Ability to operate fully independently — no engineering team underneath you — while documenting continuously and coordinating cleanly with an internal team.
Nice to Have
- Experience with API ingestion and field-level diff-detection systems.
- Laravel or PHP familiarity, enough to coordinate cleanly on API contracts with the portal team.
- SOC 2 Type II compliance experience.
- Salesforce API integration experience.
- Regulated-industry experience (financial services, healthcare, or insurance).
The expected hourly range for this position is $80/hr - 100/hr. This hourly range is an estimate, and the actual hourly rate may vary based on the Company's compensation practices. The hourly rate may be adjusted based on applicant's geographic location. This position is eligible to participate in the Company's standard employee benefits programs, which currently include health care benefits.
LI-REMOTE
QuinStreet is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, national origin, pregnancy status, sex, age, marital status, disability, sexual orientation, gender identity or any other characteristics protected by law.
Please see QuinStreet's Employee Privacy Notice here.
Powering Performance Marketplaces in Digital Media
QuinStreet is a pioneer in powering decentralized online marketplaces that match searchers and "research and compare" consumers with brands. We run these virtual- and private-label marketplaces in one of the nation's largest media networks.
Our industry leading segmentation and AI-driven matching technologies help consumers find better solutions and brands faster. They allow brands to target and reach in-market customer prospects with pinpoint segment-by-segment accuracy, and to pay only for performance results.
Our campaign-results-driven matching decision engines and optimization algorithms are built from over 20 years and billions of dollars of online media experience.
We believe in:
- The direct measurability of digital media.
- Performance marketing. (We pioneered it.)
- The advantages of technology.
We bring all this together to deliver truly great results for consumers and brands in the world's biggest channel.
Job Category
We are looking for a Senior AI Developer & Cloud Architect to design, build, and own the AI-powered compliance scraping engine and cloud infrastructure layer for an internal platform monitoring up to 70,000 credit card offer pages per month. This is a hands-on, sole-builder contractor role that sits at the intersection of cloud architecture, AI engineering, and large-scale web scraping, with a clear mandate: deliver a production-grade system that detects compliance violations across issuer offer pages with high accuracy and controlled token costs.
You will do this by architecting the AWS environment from the ground up, building a containerized worker fleet that integrates Playwright rendering with Claude-powered contextual analysis, and defining clean API contracts with the internal team that owns the Laravel control panel.
This is not a managed-PaaS or prototype role. You will be accountable for end-to-end delivery — architecture, build, documentation, and knowledge transfer — owning the full scraping and AI pipeline from URL intake through compliance findings, screenshot evidence, and results delivery back to the portal.
Responsibilities
- Design and configure the production AWS environment (ECS/Fargate, SQS, API Gateway, RDS PostgreSQL, S3, IAM, CloudWatch) using infrastructure as code (Terraform or CDK).
- Build a stateless, containerized worker fleet that integrates Playwright-based page rendering, structured rule evaluation, and Claude API analysis.
- Implement token optimization strategies across the LLM pipeline — prompt engineering, context pruning, caching, model selection, and batching — with measurable cost outcomes.
- Define and document API contracts, job payload schemas, and database write patterns with the internal Laravel portal team to enable parallel development.
- Build third-party API ingestion and field-level diff-detection logic that automatically adjusts monitoring rules when product data changes.
- Handle modern web rendering challenges at scale: JavaScript-heavy SPAs, interstitials, cookie consent overlays, dynamic content, viewport switching, and full-page screenshot capture.
- Evaluate when LLM analysis is the correct tool versus a classifier or rules-based approach, and design the two-stage rule-engine-plus-AI pipeline accordingly.
- Build and maintain a unit test suite covering all modules and APIs to ensure uptime and proper functionality.
- Document every architecture decision, configuration, API contract, and operational procedure continuously — not as a final-week deliverable.
- Deliver a complete runbook and knowledge transfer to the internal team at engagement close.
- Operate independently end-to-end while coordinating closely with the internal portal team and reporting directly to the Senior Director, surfacing risks and trade-offs early.
Requirements
- Production backend Python experience, including async patterns, type hints, packaging, and testing.
- Direct production experience designing and configuring AWS ECS/Fargate, SQS, API Gateway, RDS (PostgreSQL), S3, IAM, and CloudWatch, with infrastructure as code (Terraform or CDK).
- Real shipped systems calling the Anthropic Claude API in production, with demonstrated experience in prompt design, structured output, error handling, and cost trade-offs.
- Demonstrated track record reducing token spend on production LLM workloads, with specific before/after results you can walk through.
- Working knowledge of other LLM providers sufficient to recommend cheaper or better alternatives for specific tasks.
- Production Playwright experience at scale, including headless Chromium failure modes, network idle detection, dynamic content handling, viewport switching, and screenshot strategy. Selenium or Puppeteer experience does not substitute.
- Machine learning fundamentals sufficient to evaluate when LLM analysis is the right tool versus a classifier or rules-based approach, and to reason about evaluation and false-positive rates.
- Docker and containerization experience, including image optimization, ECR, and stateless worker design.
- Ability to operate fully independently — no engineering team underneath you — while documenting continuously and coordinating cleanly with an internal team.
Nice to Have
- Experience with API ingestion and field-level diff-detection systems.
- Laravel or PHP familiarity, enough to coordinate cleanly on API contracts with the portal team.
- SOC 2 Type II compliance experience.
- Salesforce API integration experience.
- Regulated-industry experience (financial services, healthcare, or insurance).
The expected hourly range for this position is $80/hr - 100/hr. This hourly range is an estimate, and the actual hourly rate may vary based on the Company's compensation practices. The hourly rate may be adjusted based on applicant's geographic location. This position is eligible to participate in the Company's standard employee benefits programs, which currently include health care benefits.
LI-REMOTE
QuinStreet is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, national origin, pregnancy status, sex, age, marital status, disability, sexual orientation, gender identity or any other characteristics protected by law.
Please see QuinStreet's Employee Privacy Notice here.
See all 8,101+ AI Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new AI Engineer roles.
Get Access To All JobsTips for Finding Visa Sponsorship as an AI Engineer
Position yourself at the intersection of AI and engineering
AI Engineer is a fast-growing title that bridges ML research and production software. Highlight experience deploying models, building RAG pipelines, or integrating LLMs into products - these applied skills are what companies are hiring for.
Target companies actively building AI engineering teams
Companies from startups to enterprises are creating new AI engineering roles and sponsoring H-1B petitions to fill them. Databricks, Notion, Figma, and similar companies have expanded AI headcount specifically because demand outpaces domestic supply.
Clarify your SOC code with your employer
AI Engineer roles can file under SOC 15-1252 (Software Developers) if engineering-focused or 15-2051 (Data Scientists) if research-heavy. The right choice depends on your day-to-day duties - work with your employer's immigration attorney to match accurately.
Use STEM OPT to build production AI experience
A STEM-eligible degree gives you up to 3 years of work authorization through OPT. Ship real AI features during that time - companies are far more likely to sponsor an engineer who already knows their codebase and deployment stack.
Differentiate yourself with full-stack AI skills
Many AI engineers can fine-tune models but struggle with production infrastructure. Expertise in model serving, API design, latency optimization, and monitoring makes you harder to replace and gives employers a stronger reason to sponsor.
Consider the O-1 path if you have notable AI work
Significant open-source contributions, widely used AI tools, or media coverage of your work can support an O-1 extraordinary ability petition. You don't need a PhD - demonstrated impact in applied AI can qualify.
Frequently Asked Questions
What is the difference between an AI engineer and a machine learning engineer for visa sponsorship purposes?
For immigration purposes, the distinction matters less than how the employer describes the role in the petition. AI engineer roles tend to emphasize building end-to-end AI-powered products, integrating LLMs, and designing retrieval-augmented generation systems. ML engineer roles focus more on model training infrastructure and serving pipelines. Both qualify as specialty occupations, and the key is specificity in the job description listing the technical skills required and the degree fields that prepare someone for the work.
How important is production experience versus research experience for AI engineer visa sponsorship?
Production experience is the differentiator for AI engineering roles. Employers sponsoring AI engineers are specifically looking for someone who can deploy and maintain AI systems in production, handling model versioning, building evaluation frameworks, and optimizing inference costs. While research experience shows technical depth, the visa petition for an AI engineer role will emphasize production engineering requirements. Highlight systems you have built and shipped, along with experience in tools like Kubernetes, Docker, and model serving frameworks.
Do I need a master's degree or PhD to get sponsored as an AI engineer?
A bachelor's degree in computer science or a related field is sufficient for most AI engineering positions. Unlike AI research roles where a PhD is often expected, AI engineering emphasizes practical systems-building skills over academic credentials. That said, a master's degree qualifies you for the additional 20,000 H-1B lottery slots reserved for advanced degree holders and signals deeper technical knowledge. Many successful AI engineer petitions are filed with bachelor's-level candidates who have strong production ML experience.
Which companies are most actively sponsoring AI engineers?
Companies scaling AI products are the most active sponsors. This includes large tech firms (Google, Meta, Amazon, Microsoft), AI-first startups (OpenAI, Anthropic, Cohere), and AI infrastructure companies (Databricks, Anyscale, Weights & Biases). Enterprise companies integrating AI into their products across healthcare, finance, and e-commerce are also growing sponsors. Look for employers whose job descriptions specifically mention LLM integration, RAG systems, or production AI deployment, as these roles are distinct enough to produce strong petitions.
What technical skills should I emphasize in an AI engineer visa petition?
Focus on skills that distinguish AI engineering from general software development. LLM integration, vector database management, prompt engineering pipelines, inference optimization, and retrieval-augmented generation architecture are the most relevant. Experience with evaluation frameworks for AI systems, cost optimization for GPU workloads, and monitoring for model quality in production also carries weight. The more specific the technical requirements in the petition, the stronger the specialty occupation argument.
What is the prevailing wage requirement for sponsored AI Engineer jobs?
When a U.S. employer sponsors a foreign worker for a work visa, they are legally required to pay at least the "prevailing wage", the average wage paid to workers in the same occupation, in the same geographic area, with similar experience. This is set by the Department of Labor to prevent employers from hiring foreign workers at below-market rates. The prevailing wage varies significantly by role, location, and experience level. For example, a ai engineer in California will have a different prevailing wage than the same role in a smaller state. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search Page.
How to find AI Engineer jobs with visa sponsorship?
To find AI Engineer jobs with visa sponsorship, use Migrate Mate, which specializes in connecting international tech talent with sponsoring employers. Focus on tech companies, startups, and research institutions that commonly sponsor H-1B, O-1 visa, or TN visas for AI roles. These employers actively seek machine learning engineers, data scientists, and AI researchers with specialized skills in neural networks, computer vision, and natural language processing.