Green Card Applied AI Engineer Jobs
Applied AI Engineer roles qualify for green card sponsorship under EB-2 for advanced-degree professionals or EB-3 for skilled workers, with employers filing PERM labor certification through DOL before submitting an I-140 petition to USCIS. Priority dates and country backlogs vary, so starting the sponsorship process early matters.
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Get Access To All JobsAt Apple, we focus deeply on our customers’ experience. Apple Ads brings this same approach to advertising, helping people find exactly what they’re looking for and helping advertisers grow their businesses.
Our technology powers ads and sponsorships across Apple Services, including the App Store, Apple News, and MLS Season Pass. Everything we do is designed for trust, connection, and impact: We respect user privacy, integrate advertising thoughtfully into the experience, and deliver value for advertisers of all sizes, from small app developers to global brands. Because when advertising is done right, it benefits everyone.
The Data Insights organization helps Apple Ads understand, measure, forecast, optimize, and improve the advertising ecosystem across Apple Services. We partner closely with Product, Engineering, Finance, Sales, and Leadership teams to solve complex business and product challenges using data, experimentation, statistical modeling, and machine learning.
We are looking for an Applied AI Engineer to help build and advance AI-powered platforms that transform how teams interact with data and make decisions. This role combines software engineering, machine learning, and agentic AI development to create reliable, scalable systems that make complex analytical capabilities accessible to teams across Apple Ads.
Description
As an Applied AI Engineer within Data Insights, you will help shape the next generation of AI-powered analytics and decision-support systems for Apple Ads.
You will build and evolve an AI agent platform that connects large language models (LLMs) with internal data infrastructure, analytical tools, and business knowledge to answer complex questions through natural language. Your work will span agent orchestration, context retrieval, evaluation, and production engineering, with a focus on creating reliable, scalable capabilities that other teams can build upon.
Working closely with engineers, data scientists, and business partners, you will help turn emerging AI technologies into practical tools that improve how teams access insights and make decisions.
Responsibilities
- Design and build scalable AI agent systems that connect LLMs with data infrastructure, APIs, and analytical tools.
- Advance agent orchestration, including tool calling, memory, MCPs, context retrieval, and intelligent routing.
- Develop reusable platform components that enable teams to build and extend AI-powered analytical workflows.
- Build evaluation frameworks and monitoring systems to improve agent observability, accuracy, reliability, and performance.
- Apply machine learning and analytical principles to define and improve online and offline metrics that strengthen retrieval quality, reasoning, accuracy, and traceability of AI-generated insights.
- Develop AI-native solutions from concept to production, adhering to first principles and optimizing for scalability, latency, and maintainability.
- Partner with engineers, data scientists, and business stakeholders to translate domain expertise and analytical workflows into reusable AI capabilities.
Minimum Qualifications
Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, or a related field, or equivalent practical experience.
Experience designing, building, and deploying production software systems, with strong Python programming and software engineering fundamentals.
Hands-on experience building and deploying LLM-powered applications using agentic workflows, tool calling, RAG, or related techniques.
Experience with large-scale data platforms or distributed data systems.
Solid foundation in AI, machine learning, statistics, and software engineering principles.
Strong problem-solving, communication, and cross-functional collaboration skills.
Preferred Qualifications
Experience architecting and scaling production AI agent platforms, including multi-step orchestration, tool execution, memory, and context management.
Experience developing LLM evaluation frameworks, regression benchmarks, and observability systems to improve agent reliability and reasoning quality.
Experience with advanced retrieval techniques, embeddings, model fine-tuning, or inference optimization.
Experience building natural-language-to-SQL systems, analytical agents, or AI-powered decision-support platforms.
Advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field.
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
At Apple, we focus deeply on our customers’ experience. Apple Ads brings this same approach to advertising, helping people find exactly what they’re looking for and helping advertisers grow their businesses.
Our technology powers ads and sponsorships across Apple Services, including the App Store, Apple News, and MLS Season Pass. Everything we do is designed for trust, connection, and impact: We respect user privacy, integrate advertising thoughtfully into the experience, and deliver value for advertisers of all sizes, from small app developers to global brands. Because when advertising is done right, it benefits everyone.
The Data Insights organization helps Apple Ads understand, measure, forecast, optimize, and improve the advertising ecosystem across Apple Services. We partner closely with Product, Engineering, Finance, Sales, and Leadership teams to solve complex business and product challenges using data, experimentation, statistical modeling, and machine learning.
We are looking for an Applied AI Engineer to help build and advance AI-powered platforms that transform how teams interact with data and make decisions. This role combines software engineering, machine learning, and agentic AI development to create reliable, scalable systems that make complex analytical capabilities accessible to teams across Apple Ads.
Description
As an Applied AI Engineer within Data Insights, you will help shape the next generation of AI-powered analytics and decision-support systems for Apple Ads.
You will build and evolve an AI agent platform that connects large language models (LLMs) with internal data infrastructure, analytical tools, and business knowledge to answer complex questions through natural language. Your work will span agent orchestration, context retrieval, evaluation, and production engineering, with a focus on creating reliable, scalable capabilities that other teams can build upon.
Working closely with engineers, data scientists, and business partners, you will help turn emerging AI technologies into practical tools that improve how teams access insights and make decisions.
Responsibilities
- Design and build scalable AI agent systems that connect LLMs with data infrastructure, APIs, and analytical tools.
- Advance agent orchestration, including tool calling, memory, MCPs, context retrieval, and intelligent routing.
- Develop reusable platform components that enable teams to build and extend AI-powered analytical workflows.
- Build evaluation frameworks and monitoring systems to improve agent observability, accuracy, reliability, and performance.
- Apply machine learning and analytical principles to define and improve online and offline metrics that strengthen retrieval quality, reasoning, accuracy, and traceability of AI-generated insights.
- Develop AI-native solutions from concept to production, adhering to first principles and optimizing for scalability, latency, and maintainability.
- Partner with engineers, data scientists, and business stakeholders to translate domain expertise and analytical workflows into reusable AI capabilities.
Minimum Qualifications
Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, or a related field, or equivalent practical experience.
Experience designing, building, and deploying production software systems, with strong Python programming and software engineering fundamentals.
Hands-on experience building and deploying LLM-powered applications using agentic workflows, tool calling, RAG, or related techniques.
Experience with large-scale data platforms or distributed data systems.
Solid foundation in AI, machine learning, statistics, and software engineering principles.
Strong problem-solving, communication, and cross-functional collaboration skills.
Preferred Qualifications
Experience architecting and scaling production AI agent platforms, including multi-step orchestration, tool execution, memory, and context management.
Experience developing LLM evaluation frameworks, regression benchmarks, and observability systems to improve agent reliability and reasoning quality.
Experience with advanced retrieval techniques, embeddings, model fine-tuning, or inference optimization.
Experience building natural-language-to-SQL systems, analytical agents, or AI-powered decision-support platforms.
Advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field.
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
See all 766+ Green Card Applied AI Engineer Jobs
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Get Access To All JobsTips for Finding Green Card Sponsorship as an Applied AI Engineer
Document your AI specialization precisely
PERM requires your job duties to match your credentials exactly. List specific frameworks, model architectures, and deployment environments you've worked with so your employer can build a labor certification that reflects your actual role, not a generic software engineer description.
Target employers with PERM filing history
Search OFLC Wage Search to verify that a prospective employer has filed PERM applications for AI or machine learning roles before. Employers new to PERM sponsorship often underestimate the timeline, which can stall your case after you've already joined.
Find green card sponsors on Migrate Mate
Use Migrate Mate to filter Applied AI Engineer roles by employers who actively sponsor EB-2 and EB-3 green cards. Seeing verified sponsorship history upfront saves you from raising the subject with employers who have no PERM infrastructure in place.
Clarify EB-2 versus EB-3 with your employer early
Applied AI Engineer positions can qualify under either category depending on how the employer defines minimum requirements. EB-2 requires a master's degree or equivalent, while EB-3 covers roles requiring a bachelor's. The category affects your priority date and, for some nationalities, your wait time significantly.
Request the PERM start date before accepting an offer
Ask specifically when your employer plans to begin the prevailing wage determination with DOL. PERM requires a formal PWD before recruitment can start, and that process alone can take several months, pushing your I-140 filing further than most candidates expect.
Check your O*NET occupation profile against your job duties
USCIS scrutinizes whether Applied AI Engineer duties constitute a specialty occupation. Reviewing the O*NET profile for your SOC code helps you confirm that your role aligns with published degree requirements, which strengthens both the PERM application and any subsequent RFE response.
Green Card Applied AI Engineer: Frequently Asked Questions
Do Applied AI Engineer roles qualify for EB-2 or EB-3 green card sponsorship?
Most Applied AI Engineer positions qualify under EB-2 if the employer requires a master's degree or you can demonstrate the equivalent through a combination of a bachelor's degree and five or more years of progressive experience. EB-3 applies when the role requires a bachelor's degree as the minimum. The classification affects your priority date and, for nationals of high-backlog countries like India and China, can meaningfully change your wait time.
How does the green card process differ from H-1B sponsorship for this role?
H-1B visa sponsorship grants temporary status in two-year or three-year increments and is subject to annual lottery caps. The EB-2 and EB-3 green card process leads to permanent residency and has no annual employer cap for most nationalities, though per-country limits create backlogs for applicants from India and China. The PERM labor certification process also requires your employer to conduct a formal recruitment campaign before filing, which adds three to twelve months before USCIS even receives the I-140 petition.
How long does the PERM process typically take for an Applied AI Engineer?
The DOL prevailing wage determination currently runs several months, followed by the supervised recruitment period of at least 30 days, then PERM audit risk adds further variance. From the day your employer begins the PWD request to an approved I-140 at USCIS, plan for 18 to 30 months in a standard case without an audit. Premium processing is available for I-140 but not for PERM, so the DOL phase cannot be accelerated.
What should I look for in an employer willing to sponsor a green card?
Look for employers who have filed PERM applications for AI or machine learning roles in the past two years, have dedicated immigration counsel rather than ad hoc vendor relationships, and are willing to state a clear PERM start date in your offer letter. You can search for Applied AI Engineer roles at employers with verified green card sponsorship history on Migrate Mate, which filters by sponsorship type so you're only seeing companies that have already committed to the process.
Can I change jobs after my employer files my PERM application?
Changing employers resets the PERM process entirely because the application is tied to a specific employer and a specific job description. If your I-140 has been approved and has been pending for 180 days or more, portability rules under AC21 allow you to move to a similar role at a new employer without losing your priority date. For most Applied AI Engineer candidates early in the process, leaving before I-140 approval means starting over with a new sponsor.