Applied AI Engineer Green Card 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.
See All Applied AI Engineer JobsOverview
Showing 5 of 362+ Applied AI Engineer jobs
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
See all 362+ Applied AI Engineer jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Applied AI Engineer roles.
Get Access To All JobsAbout Quizlet:
At Quizlet, our mission is to help every learner achieve their outcomes in the most effective and delightful way. We’re a $1B+ learning platform used by two-thirds of U.S. high school students and half of college students, powering over 1 billion learning interactions each week.
We blend cognitive science with machine learning to personalize and enhance the learning experience for students, professionals, and lifelong learners alike. We’re energized by the potential to power more learners through multiple approaches and various tools. Let’s Build the Future of Learning
Join us to design and deliver AI-powered learning tools that scale across the world and unlock human potential.
About the Team (Applied AI):
Our mission is to invent and deploy the next generation of intelligent, personalized, and adaptive learning experiences. We’re consolidating AI efforts across the company into a unified portfolio and are accountable for a disproportionate share of Quizlet’s growth and product differentiation. You’ll partner closely with Product, Data Science, and the AI & Data Platform to deliver an AI-driven learning coach that’s recognized as best-in-class.
About the Role:
We are looking for Applied AI Engineers ranging from the Senior to Staff as well as Sr. Staff levels (note: leveling decisions made through the interview process).
You’ll be working at the forefront of our AI strategy, shaping Quizlet’s AI development in one of the two complementary domains:
Personalization & Ranking – retrieval and ranking systems that match learners with the right content, experiences, and monetization moments across surfaces (search, feed, notifications, ads).
Generative AI & Agentic Systems – LLM-powered tutoring, content understanding/synthesis, and tools that boost learner outcomes and creator productivity.
You will work on a variety of models and modeling systems (from Two-Tower retrieval and multi-task rankers to RAG/LLM pipelines), ensure robust evaluation, and responsible deployment.
We’re happy to share that this is an onsite position in either our Denver, San Francisco, Seattle, or NYC. To help foster team collaboration, we require that employees be in the office a minimum of three days per week: Monday, Wednesday, and Thursday and as needed by your manager or the company. We believe that this working environment facilitates increased work efficiency, team partnership, and supports growth as an employee and organization.
In this role, you will:
- Contribute to the technical roadmap for applied AI across personalization, ranking, search, recommendations, and GenAI/LLM systems; help connect modeling work to business metrics (engaged learners, conversion, retention, revenue)
- Build components of end-to-end ML systems: candidate sourcing, embedding platforms & ANN retrieval, multi-stage ranking (early/late), and value modeling with guardrails for fairness and integrity
- Implement LLM-based features: build RAG pipelines, apply instruction-/preference-tuning techniques (e.g., SFT/DPO), optimize prompts, and improve latency/cost-aware inference; contribute to offline evals + human-in-the-loop and online success metrics
- Help develop "Learner 360" representations by working with behavior signals, explicit inputs, and conversational context to create robust embeddings reused across surfaces
- Support evaluation infrastructure: contribute to the eval harness for both ranking and generative systems (offline metrics like NDCG/AUC/BLEU/BERTScore; quality/safety scorecards), and help close the loop with online A/B experiments
- Ship reliable systems at scale: ensure training-serving consistency, implement drift detection, follow canarying/rollback protocols, participate in on-call rotation for model services, and maintain strong CI/CD for features & models
- Collaborate with and learn from senior ML/SWE teammates; write high-quality code and follow best practices for experimentation rigor and reproducibility
- Work closely with Product, Design, Legal, and Data Science on objectives, tradeoffs, and responsible AI practices
- Stay current with ML research (RecSys, LLMs, multimodal) and propose new methods that could improve learner outcomes
What you bring to the table:
- 6+ years of industry experience in applied ML/AI or ML-heavy software engineering
- BS/MS in CS, ML, or related quantitative field (or equivalent experience)
- Experience building ranking/personalization or search systems (retrieval, Two-Tower/dual encoders, multi-task rankers) and contributing to online metric improvements (e.g., CTR, session depth, retention)
- Hands-on experience with LLM/GenAI systems: data curation, fine-tuning (SFT/PEFT, preference optimization), prompt engineering, evaluation, and productionization considerations (latency/cost/safety)
- Strong skills in Python/PyTorch, data and feature engineering, distributed training/inference on GPUs, and familiarity with modern MLOps (model registry, feature stores, monitoring, drift)
- Solid experiment design (offline/online), metrics literacy, and ability to translate product goals into modeling solutions
- Strong collaboration skills and eagerness to learn from senior engineers; some experience mentoring junior teammates is a plus
Bonus points if you have:
- EdTech or consumer mobile experience; conversational tutoring or learning science-informed modeling
- Publications/open-source with RecSys/LLMs (e.g., RecSys, KDD, NeurIPS, ICLR, ACL), or contributions to safety/guardrails tooling
- Experience building on a modern MLOps stack (feature mgmt, orchestration, streaming, online inference at scale)
Compensation, Benefits & Perks:
- Quizlet is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Salary transparency helps to mitigate unfair hiring practices when it comes to discrimination and pay gaps. Total compensation for this role is market competitive, including a starting base salary of $178,000 - $330,000 depending on location and experience, as well as company stock options
- Collaborate with your manager and team to create a healthy work-life balance
- 20 vacation days that we expect you to take!
- Competitive health, dental, and vision insurance (100% employee and 75% dependent PPO, Dental, VSP Choice)
- Employer-sponsored 401k plan with company match
- Access to LinkedIn Learning and other resources to support professional growth
- Paid Family Leave, FSA, HSA, Commuter benefits, and Wellness benefits
- 40 hours of annual paid time off to participate in volunteer programs of choice
Why Join Quizlet?
🌎 Massive reach: 60M+ users, 1B+ interactions per week
🤖 Cutting-edge tech: Generative AI, adaptive learning, cognitive science
📈 Strong momentum: Top-tier investors, sustainable business, real traction
🎨 Mission-first: Work that makes a difference in people’s lives
🤝 Inclusive culture: Committed to equity, diversity, and belonging We strive to make everyone feel comfortable and welcome!
We work to create a holistic interview process, where both Quizlet and candidates have an opportunity to view what it would be like to work together, in exploring a mutually beneficial partnership.
We provide a transparent setting that gives a comprehensive view of who we are!
In Closing:
At Quizlet, we’re excited about passionate people joining our team—even if you don’t check every box on the requirements list. We value unique perspectives and believe everyone has something meaningful to contribute. Our culture is all about taking initiative, learning through challenges, and striving for high-quality work while staying curious and open to new ideas. We believe in honest, respectful communication, thoughtful collaboration, and creating a supportive space where everyone can grow and succeed together.
Quizlet’s success as an online learning community depends on a strong commitment to diversity, equity, and inclusion.
As an equal opportunity employer and a tech company committed to societal change, we welcome applicants from all backgrounds. Women, people of color, members of the LGBTQ+ community, individuals with disabilities, and veterans are strongly encouraged to apply. Come join us!
To All Recruiters and Placement Agencies:
At this time, Quizlet does not accept unsolicited agency resumes and/or profiles. Please do not forward unsolicited agency resumes to our website or to any Quizlet employee. Quizlet will not pay fees to any third-party agency or firm nor will it be responsible for any agency fees associated with unsolicited resumes. All unsolicited resumes received will be considered the property of Quizlet.
LI-onsite
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
About Quizlet:
At Quizlet, our mission is to help every learner achieve their outcomes in the most effective and delightful way. We’re a $1B+ learning platform used by two-thirds of U.S. high school students and half of college students, powering over 1 billion learning interactions each week.
We blend cognitive science with machine learning to personalize and enhance the learning experience for students, professionals, and lifelong learners alike. We’re energized by the potential to power more learners through multiple approaches and various tools. Let’s Build the Future of Learning
Join us to design and deliver AI-powered learning tools that scale across the world and unlock human potential.
About the Team (Applied AI):
Our mission is to invent and deploy the next generation of intelligent, personalized, and adaptive learning experiences. We’re consolidating AI efforts across the company into a unified portfolio and are accountable for a disproportionate share of Quizlet’s growth and product differentiation. You’ll partner closely with Product, Data Science, and the AI & Data Platform to deliver an AI-driven learning coach that’s recognized as best-in-class.
About the Role:
We are looking for Applied AI Engineers ranging from the Senior to Staff as well as Sr. Staff levels (note: leveling decisions made through the interview process).
You’ll be working at the forefront of our AI strategy, shaping Quizlet’s AI development in one of the two complementary domains:
Personalization & Ranking – retrieval and ranking systems that match learners with the right content, experiences, and monetization moments across surfaces (search, feed, notifications, ads).
Generative AI & Agentic Systems – LLM-powered tutoring, content understanding/synthesis, and tools that boost learner outcomes and creator productivity.
You will work on a variety of models and modeling systems (from Two-Tower retrieval and multi-task rankers to RAG/LLM pipelines), ensure robust evaluation, and responsible deployment.
We’re happy to share that this is an onsite position in either our Denver, San Francisco, Seattle, or NYC. To help foster team collaboration, we require that employees be in the office a minimum of three days per week: Monday, Wednesday, and Thursday and as needed by your manager or the company. We believe that this working environment facilitates increased work efficiency, team partnership, and supports growth as an employee and organization.
In this role, you will:
- Contribute to the technical roadmap for applied AI across personalization, ranking, search, recommendations, and GenAI/LLM systems; help connect modeling work to business metrics (engaged learners, conversion, retention, revenue)
- Build components of end-to-end ML systems: candidate sourcing, embedding platforms & ANN retrieval, multi-stage ranking (early/late), and value modeling with guardrails for fairness and integrity
- Implement LLM-based features: build RAG pipelines, apply instruction-/preference-tuning techniques (e.g., SFT/DPO), optimize prompts, and improve latency/cost-aware inference; contribute to offline evals + human-in-the-loop and online success metrics
- Help develop "Learner 360" representations by working with behavior signals, explicit inputs, and conversational context to create robust embeddings reused across surfaces
- Support evaluation infrastructure: contribute to the eval harness for both ranking and generative systems (offline metrics like NDCG/AUC/BLEU/BERTScore; quality/safety scorecards), and help close the loop with online A/B experiments
- Ship reliable systems at scale: ensure training-serving consistency, implement drift detection, follow canarying/rollback protocols, participate in on-call rotation for model services, and maintain strong CI/CD for features & models
- Collaborate with and learn from senior ML/SWE teammates; write high-quality code and follow best practices for experimentation rigor and reproducibility
- Work closely with Product, Design, Legal, and Data Science on objectives, tradeoffs, and responsible AI practices
- Stay current with ML research (RecSys, LLMs, multimodal) and propose new methods that could improve learner outcomes
What you bring to the table:
- 6+ years of industry experience in applied ML/AI or ML-heavy software engineering
- BS/MS in CS, ML, or related quantitative field (or equivalent experience)
- Experience building ranking/personalization or search systems (retrieval, Two-Tower/dual encoders, multi-task rankers) and contributing to online metric improvements (e.g., CTR, session depth, retention)
- Hands-on experience with LLM/GenAI systems: data curation, fine-tuning (SFT/PEFT, preference optimization), prompt engineering, evaluation, and productionization considerations (latency/cost/safety)
- Strong skills in Python/PyTorch, data and feature engineering, distributed training/inference on GPUs, and familiarity with modern MLOps (model registry, feature stores, monitoring, drift)
- Solid experiment design (offline/online), metrics literacy, and ability to translate product goals into modeling solutions
- Strong collaboration skills and eagerness to learn from senior engineers; some experience mentoring junior teammates is a plus
Bonus points if you have:
- EdTech or consumer mobile experience; conversational tutoring or learning science-informed modeling
- Publications/open-source with RecSys/LLMs (e.g., RecSys, KDD, NeurIPS, ICLR, ACL), or contributions to safety/guardrails tooling
- Experience building on a modern MLOps stack (feature mgmt, orchestration, streaming, online inference at scale)
Compensation, Benefits & Perks:
- Quizlet is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Salary transparency helps to mitigate unfair hiring practices when it comes to discrimination and pay gaps. Total compensation for this role is market competitive, including a starting base salary of $178,000 - $330,000 depending on location and experience, as well as company stock options
- Collaborate with your manager and team to create a healthy work-life balance
- 20 vacation days that we expect you to take!
- Competitive health, dental, and vision insurance (100% employee and 75% dependent PPO, Dental, VSP Choice)
- Employer-sponsored 401k plan with company match
- Access to LinkedIn Learning and other resources to support professional growth
- Paid Family Leave, FSA, HSA, Commuter benefits, and Wellness benefits
- 40 hours of annual paid time off to participate in volunteer programs of choice
Why Join Quizlet?
🌎 Massive reach: 60M+ users, 1B+ interactions per week
🤖 Cutting-edge tech: Generative AI, adaptive learning, cognitive science
📈 Strong momentum: Top-tier investors, sustainable business, real traction
🎨 Mission-first: Work that makes a difference in people’s lives
🤝 Inclusive culture: Committed to equity, diversity, and belonging We strive to make everyone feel comfortable and welcome!
We work to create a holistic interview process, where both Quizlet and candidates have an opportunity to view what it would be like to work together, in exploring a mutually beneficial partnership.
We provide a transparent setting that gives a comprehensive view of who we are!
In Closing:
At Quizlet, we’re excited about passionate people joining our team—even if you don’t check every box on the requirements list. We value unique perspectives and believe everyone has something meaningful to contribute. Our culture is all about taking initiative, learning through challenges, and striving for high-quality work while staying curious and open to new ideas. We believe in honest, respectful communication, thoughtful collaboration, and creating a supportive space where everyone can grow and succeed together.
Quizlet’s success as an online learning community depends on a strong commitment to diversity, equity, and inclusion.
As an equal opportunity employer and a tech company committed to societal change, we welcome applicants from all backgrounds. Women, people of color, members of the LGBTQ+ community, individuals with disabilities, and veterans are strongly encouraged to apply. Come join us!
To All Recruiters and Placement Agencies:
At this time, Quizlet does not accept unsolicited agency resumes and/or profiles. Please do not forward unsolicited agency resumes to our website or to any Quizlet employee. Quizlet will not pay fees to any third-party agency or firm nor will it be responsible for any agency fees associated with unsolicited resumes. All unsolicited resumes received will be considered the property of Quizlet.
LI-onsite
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
See all 362+ Applied AI Engineer jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Applied AI Engineer roles.
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.
Applied AI Engineer jobs are hiring across the US. Find yours.
Find Applied AI Engineer JobsApplied AI Engineer Green Card Sponsorship: 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 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.
See which Applied AI Engineer employers are hiring and sponsoring visas right now.
Search Applied AI Engineer Jobs