OPT AI Engineer Jobs
AI Engineer jobs are among the most actively sponsored roles for OPT students, with demand concentrated in tech, finance, and healthcare. Most positions require Python proficiency and machine learning fundamentals. Standard OPT gives you 12 months, but a STEM extension adds 24 more, covering most H-1B visa timelines.
Find OPT AI Engineer JobsOverview
Showing 5 of 1,402+ AI Engineer jobs
See all 1,402+ 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 JobsAbout Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About The Role
Anthropic is seeking an AI Fluency Education Lead to create courses and content that teach people how AI works and how to work well with AI. Most people right now pick up AI wherever they happen to find it: a prompt from a coworker, a video promising ten hacks, or a screenshot from LinkedIn. It’s hard to discern which tidbits are short term tactics and what skills will be enduring. AI Fluency is our work to close that gap: teaching how AI actually works, and how to work well alongside it, so people can make their own decisions and judgements with AI instead of copying someone else’s. Note that this does not cover product training. We recognize that most people do not use a single tool or single model, so AI Fluency is deliberately model agnostic. We teach durable mindsets and understandings that help someone use AI well and safely, no matter what system is in front of them. Much of this role is about creation: writing, filming, prototyping, and publishing. It’s also about building the systems that let that work multiply and reach the right audiences. We can't teach every audience ourselves, so you'd design a modular library — short videos, exercises, frameworks, one-pagers, all free and openly licensed — along with the AI-assisted pipelines that remix and assemble those pieces into something specific for a given learner. You will also work with other teams to decide which audiences we go after, which topics we prioritize, and which formats we pursue. You may also invent formats that don't exist yet, because most AI education still looks like an online course from 2015 and we don't think the subject and the medium should be that far apart. You would work closely with a sister group on the education team that researches what fluency means and how to measure it. You’re the person who takes that and turns it into something someone can actually learn from.
Key Responsibilities
- Own the curriculum for general-public AI fluency end to end — the roadmap, the sequencing, the quality bar, and the call on what we teach and what we deliberately leave out.
- Curate and create the AI fluency material itself — curricula, courses, videos, essays, exercises, interactive lessons — and set the quality bar for any partner content creation engagements.
- Build AI-assisted production pipelines so the distance from an idea to a published piece keeps getting shorter while the quality keeps going up.
- Turn research frameworks into material that resonates for people whose jobs look nothing like ours: nurses, teachers, small business owners, policymakers, community leaders, and so on.
- Learn from and co-create with outside institutions, such as educators, public-sector organizations, and community groups.
- Prototype learning formats that only work with AI in the loop — personalized paths, conversational practice, assessment that adapts to the learner — and get rough versions in front of real people in days rather than quarters.
- Measure whether the teaching worked — completion is not comprehension, and comprehension is not changed behavior. Define what learning actually looks like for this audience and instrument for it.
You may be a good fit if you have
- Deep experience designing content and curricula for adult learners at significant scale, with real taste for what makes learning stick when you’re not in the room.
- Exceptional writing for general audiences, and specifically the knack for turning a technical idea into a mental model that's accurate and also intuitive to novices and those who have never seen it before.
- Experience making multimedia learning content end to end — scripts, video, interactive — and working with agencies and partners to deliver on multiple projects in short deadlines.
- A working practice of using Claude and other LLMs as infrastructure in your own production.
- Enough technical comfort to build light tooling and automations yourself; this isn't an engineering role, but you shouldn't be intimidated by adding to the pipeline that produces your work.
- Experience-derived opinions about pedagogy you'll argue for, and the willingness to drop them when the data says otherwise.
- Genuine satisfaction in making things other people teach, such as a teacher taking your material, changing it for their audience, and delivering education without your oversight.
- Comfort in a fast-moving environment where you're building the process as you go.
You don't need a background in AI or a degree in education, but you do need to have made people fluent in something before at scale.
Strong candidates may also have
- Built an education program or content function from nothing.
- Experience in public education, civic technology, or policy communication.
- A background in AI/ML education, learning science, cognitive science, or behavioral research.
- Experience co-creating content with outside institutions and partners, not only with vendors and production agencies.
- Experience building AI-augmented content pipelines.
The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary
$270,000—$365,000 USD
Logistics
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience.
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience.
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position.
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us.
To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
How We're Different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.
Guidance on Candidates' AI Usage:
Learn about our policy for using AI in our application process.
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About The Role
Anthropic is seeking an AI Fluency Education Lead to create courses and content that teach people how AI works and how to work well with AI. Most people right now pick up AI wherever they happen to find it: a prompt from a coworker, a video promising ten hacks, or a screenshot from LinkedIn. It’s hard to discern which tidbits are short term tactics and what skills will be enduring. AI Fluency is our work to close that gap: teaching how AI actually works, and how to work well alongside it, so people can make their own decisions and judgements with AI instead of copying someone else’s. Note that this does not cover product training. We recognize that most people do not use a single tool or single model, so AI Fluency is deliberately model agnostic. We teach durable mindsets and understandings that help someone use AI well and safely, no matter what system is in front of them. Much of this role is about creation: writing, filming, prototyping, and publishing. It’s also about building the systems that let that work multiply and reach the right audiences. We can't teach every audience ourselves, so you'd design a modular library — short videos, exercises, frameworks, one-pagers, all free and openly licensed — along with the AI-assisted pipelines that remix and assemble those pieces into something specific for a given learner. You will also work with other teams to decide which audiences we go after, which topics we prioritize, and which formats we pursue. You may also invent formats that don't exist yet, because most AI education still looks like an online course from 2015 and we don't think the subject and the medium should be that far apart. You would work closely with a sister group on the education team that researches what fluency means and how to measure it. You’re the person who takes that and turns it into something someone can actually learn from.
Key Responsibilities
- Own the curriculum for general-public AI fluency end to end — the roadmap, the sequencing, the quality bar, and the call on what we teach and what we deliberately leave out.
- Curate and create the AI fluency material itself — curricula, courses, videos, essays, exercises, interactive lessons — and set the quality bar for any partner content creation engagements.
- Build AI-assisted production pipelines so the distance from an idea to a published piece keeps getting shorter while the quality keeps going up.
- Turn research frameworks into material that resonates for people whose jobs look nothing like ours: nurses, teachers, small business owners, policymakers, community leaders, and so on.
- Learn from and co-create with outside institutions, such as educators, public-sector organizations, and community groups.
- Prototype learning formats that only work with AI in the loop — personalized paths, conversational practice, assessment that adapts to the learner — and get rough versions in front of real people in days rather than quarters.
- Measure whether the teaching worked — completion is not comprehension, and comprehension is not changed behavior. Define what learning actually looks like for this audience and instrument for it.
You may be a good fit if you have
- Deep experience designing content and curricula for adult learners at significant scale, with real taste for what makes learning stick when you’re not in the room.
- Exceptional writing for general audiences, and specifically the knack for turning a technical idea into a mental model that's accurate and also intuitive to novices and those who have never seen it before.
- Experience making multimedia learning content end to end — scripts, video, interactive — and working with agencies and partners to deliver on multiple projects in short deadlines.
- A working practice of using Claude and other LLMs as infrastructure in your own production.
- Enough technical comfort to build light tooling and automations yourself; this isn't an engineering role, but you shouldn't be intimidated by adding to the pipeline that produces your work.
- Experience-derived opinions about pedagogy you'll argue for, and the willingness to drop them when the data says otherwise.
- Genuine satisfaction in making things other people teach, such as a teacher taking your material, changing it for their audience, and delivering education without your oversight.
- Comfort in a fast-moving environment where you're building the process as you go.
You don't need a background in AI or a degree in education, but you do need to have made people fluent in something before at scale.
Strong candidates may also have
- Built an education program or content function from nothing.
- Experience in public education, civic technology, or policy communication.
- A background in AI/ML education, learning science, cognitive science, or behavioral research.
- Experience co-creating content with outside institutions and partners, not only with vendors and production agencies.
- Experience building AI-augmented content pipelines.
The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary
$270,000—$365,000 USD
Logistics
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience.
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience.
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position.
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us.
To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
How We're Different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.
Guidance on Candidates' AI Usage:
Learn about our policy for using AI in our application process.
See all 1,402+ OPT AI Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new OPT AI Engineer Jobs.
Get Access To All JobsTips for Finding OPT Sponsorship as an AI Engineer
Target STEM OPT-eligible roles explicitly
AI Engineer falls under CIP code 11.0101 (Computer Science) in most cases, qualifying you for the 24-month STEM extension. Confirm your degree CIP code with your DSO before applying so you can communicate your full 36-month authorization window to employers.
Lead with your authorization timeline, not just your status
Telling a recruiter you have 36 months of work authorization is more compelling than saying you're on OPT. Frame it as a long runway that covers multiple H-1B lottery cycles, which directly addresses the sponsorship risk employers are weighing.
Focus on employers with active H-1B filing histories
Companies that have consistently filed H-1B petitions for engineering roles are far more likely to sponsor you after OPT. Prioritize mid-size and enterprise tech firms with established immigration infrastructure over early-stage startups without prior sponsorship experience.
Specialize in a high-demand AI subfield
Generalist AI skills are increasingly commoditized. Employers sponsor OPT workers when they see skills that are hard to find. Depth in LLM fine-tuning, computer vision, reinforcement learning, or ML infrastructure makes you a more defensible sponsorship investment for hiring managers.
File your STEM extension application at least 90 days before OPT expires
USCIS allows you to apply up to 90 days before your OPT end date. Filing early protects your work authorization if USCIS processing runs long. Late filing can create gaps that jeopardize your employment, so coordinate with your DSO well in advance.
Quantify your ML work in concrete business terms
Recruiters and hiring managers respond to outcomes, not methods. Instead of listing model architectures, describe what your work produced: reduced inference latency by 40%, improved recommendation CTR, or cut data pipeline costs. Business impact makes the sponsorship case easier to justify internally.
AI Engineer OPT: Frequently Asked Questions
Can F-1 OPT students work as AI Engineers in the United States?
Yes. AI Engineer is a STEM-qualifying role, which means F-1 students with a degree in computer science, data science, or a related field can work in this role on standard 12-month OPT and then apply for the 24-month STEM extension. That gives you up to 36 months of work authorization without employer sponsorship during that period.
Does the AI Engineer role qualify for the STEM OPT extension?
In most cases, yes. STEM OPT eligibility depends on your degree's CIP code, not the job title itself. If your degree is in computer science, electrical engineering, mathematics, or a related STEM field, you'll almost certainly qualify. Confirm with your DSO before accepting an offer, since your school must authorize the extension and your employer must be enrolled in E-Verify.
How do I find AI Engineer jobs that sponsor OPT or H-1B visas?
Migrate Mate is built specifically for this search. It filters for employers with active visa sponsorship histories, so you're not cold-applying to companies that won't sponsor. AI Engineer roles on the platform are sourced from employers who have previously filed H-1B petitions for engineering positions, which is the strongest proxy for willingness to sponsor OPT workers transitioning to H-1B.
What happens to my OPT authorization if my AI Engineer job ends?
You have a 60-day grace period after your employment ends during which you can remain in the U.S. to find new work. If you're on STEM OPT, the same 60-day rule applies. You must report unemployment through your DSO. Extended unemployment beyond 90 days total during standard OPT (or 150 days during STEM OPT) can jeopardize your status, so act quickly.
Do AI Engineer employers typically sponsor H-1B visas after OPT ends?
Many do, particularly mid-size and large technology companies with dedicated immigration programs. The H-1B lottery runs in March for an October 1 start date, so timing matters. AI Engineers with specialized skills in areas like LLM development, MLOps, or computer vision tend to have stronger sponsorship cases. Starting the H-1B conversation with your employer six to nine months before your OPT expires gives both sides adequate planning time.