STEM OPT Applied AI Engineer Jobs
Applied AI Engineer roles sit squarely within STEM degree categories that qualify for the 24-month STEM OPT extension, giving you up to 36 months of F-1 work authorization. Every employer you work for must be enrolled in E-Verify and sign a formal training plan with your DSO before your extension starts.
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Get Access To All JobsINTRODUCTION
Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster. We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI. We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services. If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe.
ABOUT THE ROLE
You will spend your time making large language models run faster, cheaper, and more reliably in production. That means owning the inference stack end to end: profiling where time and cost go, bringing modern optimization techniques into real deployments, and getting deep into the serving code when the defaults are not good enough. This is core systems and performance work on some of the most demanding models in use today. The work is applied, not academic. The optimizations you build land in real customer deployments, each with its own models, traffic patterns, latency targets, and cost constraints. So while performance is the heart of the role, you will also work directly with customer engineering teams to tailor deployments to their needs, take a workload from an early proof of concept to a fully monitored production service, and make sure the gains you engineer actually show up for the people running the workload. To set expectations clearly, this is a hands-on engineering role built around coding, profiling, and low-level optimization. It also carries a customer-facing side, along with elements of product and technical solutions work, because that is where the performance work gets proven.
WHAT YOU'LL BE WORKING ON:
- Bring current inference techniques into production and refine them.
- Design and optimize serving architectures, including prefill and decode disaggregation, request routing, and related approaches.
- Work down into the serving stack, from frameworks like vLLM and SGLang to the CUDA kernels underneath, profiling and running in-depth analysis to find and fix performance problems.
- Adapt and scale optimization methods across many kinds of ML models, with an emphasis on large language models.
- Profile and tune deployments against clear targets for latency, throughput, and cost, and keep them dependable under real traffic.
- Tailor deployments to each customer's models and constraints, partnering with their engineering teams to move a workload from an early proof of concept through to a live, well-monitored production service.
- Build and support the software and product features around the inference stack in a production setting, using one or more general-purpose languages, with Python preferred given how central it is to ML work.
- Experiment quickly: take fuzzy goals, shape them into clear specs and focused proofs of concept, run fast experiments to find what works, and ship well-tested results without delay.
- Own delivery end to end, from the first experiment through to the optimization running in production, keeping the underlying performance goals, clear specs, and follow-through front of mind, and drafting features and product requirement documents together with other engineering and product teams.
- Work through ambiguity and make sound calls on tradeoffs and tooling, steering away from complexity that is not needed.
- Take real pride and ownership in your work, hold yourself accountable, and look for the same from the people around you.
WHAT YOU'LL BRING TO THE TEAM:
- A Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, Mathematics, or a related field.
- Hands-on experience shipping code in production with one or more general-purpose languages, such as Python or C++, with a strong preference for Python.
- Familiarity with methods for optimizing LLMs for high throughput / low latency inference.
- Comfort with modern LLM serving frameworks such as vLLM or SGLang, and with profiling and analyzing performance down to the kernel level.
- A firm grasp of how GPUs are built and how they behave.
- Clear interest and hands-on experience with large language models.
- A working knowledge of AI/ML pipelines and the full path of developing and deploying ML models.
- Strong communication skills, particularly when explaining hard technical topics to customers and teammates.
BONUS POINTS:
- A track record of making software systems run faster, especially for large language models.
- Experience with CUDA or comparable technologies.
- A strong command of software engineering fundamentals, with a record of building and shipping AI/ML inference systems.
- Experience with Docker and Kubernetes.
- Prior work building or tuning AI/ML projects, particularly in a customer-facing setting.
BENEFITS:
- Competitive compensation and equity packages
- Restricted Stock Units
- Paid time off, paid holidays & leave of absence programs
- Comprehensive health, dental & vision insurance
- Employer contributions to HSA account
- Paid parental leave
- Paid life insurance, short-term and long-term disability
- Professional development & tuition reimbursement
- Mental health & wellness support
- Commuter benefits (parking & transit)
- Cell phone stipend
- 401(k) Retirement plan with company match up to 4% of salary
- Volunteer time off
- Global travel insurance & emergency assistance
- Daily meals allowance
- Additional perks & programs specific to location
COMPENSATION RANGE
Compensation will be paid in the range of up to $185,000 - $225,000 + Bonus. Restricted Stock Units are included in all offers. Compensation to be determined by the applicant's knowledge, education, and abilities, as well as internal equity and alignment with market data.
Crusoe is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, disability, genetic information, pregnancy, citizenship, marital status, sex/gender, sexual preference/orientation, gender identity, age, veteran status, national origin, or any other status protected by law or regulation.
INTRODUCTION
Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster. We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI. We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services. If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe.
ABOUT THE ROLE
You will spend your time making large language models run faster, cheaper, and more reliably in production. That means owning the inference stack end to end: profiling where time and cost go, bringing modern optimization techniques into real deployments, and getting deep into the serving code when the defaults are not good enough. This is core systems and performance work on some of the most demanding models in use today. The work is applied, not academic. The optimizations you build land in real customer deployments, each with its own models, traffic patterns, latency targets, and cost constraints. So while performance is the heart of the role, you will also work directly with customer engineering teams to tailor deployments to their needs, take a workload from an early proof of concept to a fully monitored production service, and make sure the gains you engineer actually show up for the people running the workload. To set expectations clearly, this is a hands-on engineering role built around coding, profiling, and low-level optimization. It also carries a customer-facing side, along with elements of product and technical solutions work, because that is where the performance work gets proven.
WHAT YOU'LL BE WORKING ON:
- Bring current inference techniques into production and refine them.
- Design and optimize serving architectures, including prefill and decode disaggregation, request routing, and related approaches.
- Work down into the serving stack, from frameworks like vLLM and SGLang to the CUDA kernels underneath, profiling and running in-depth analysis to find and fix performance problems.
- Adapt and scale optimization methods across many kinds of ML models, with an emphasis on large language models.
- Profile and tune deployments against clear targets for latency, throughput, and cost, and keep them dependable under real traffic.
- Tailor deployments to each customer's models and constraints, partnering with their engineering teams to move a workload from an early proof of concept through to a live, well-monitored production service.
- Build and support the software and product features around the inference stack in a production setting, using one or more general-purpose languages, with Python preferred given how central it is to ML work.
- Experiment quickly: take fuzzy goals, shape them into clear specs and focused proofs of concept, run fast experiments to find what works, and ship well-tested results without delay.
- Own delivery end to end, from the first experiment through to the optimization running in production, keeping the underlying performance goals, clear specs, and follow-through front of mind, and drafting features and product requirement documents together with other engineering and product teams.
- Work through ambiguity and make sound calls on tradeoffs and tooling, steering away from complexity that is not needed.
- Take real pride and ownership in your work, hold yourself accountable, and look for the same from the people around you.
WHAT YOU'LL BRING TO THE TEAM:
- A Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, Mathematics, or a related field.
- Hands-on experience shipping code in production with one or more general-purpose languages, such as Python or C++, with a strong preference for Python.
- Familiarity with methods for optimizing LLMs for high throughput / low latency inference.
- Comfort with modern LLM serving frameworks such as vLLM or SGLang, and with profiling and analyzing performance down to the kernel level.
- A firm grasp of how GPUs are built and how they behave.
- Clear interest and hands-on experience with large language models.
- A working knowledge of AI/ML pipelines and the full path of developing and deploying ML models.
- Strong communication skills, particularly when explaining hard technical topics to customers and teammates.
BONUS POINTS:
- A track record of making software systems run faster, especially for large language models.
- Experience with CUDA or comparable technologies.
- A strong command of software engineering fundamentals, with a record of building and shipping AI/ML inference systems.
- Experience with Docker and Kubernetes.
- Prior work building or tuning AI/ML projects, particularly in a customer-facing setting.
BENEFITS:
- Competitive compensation and equity packages
- Restricted Stock Units
- Paid time off, paid holidays & leave of absence programs
- Comprehensive health, dental & vision insurance
- Employer contributions to HSA account
- Paid parental leave
- Paid life insurance, short-term and long-term disability
- Professional development & tuition reimbursement
- Mental health & wellness support
- Commuter benefits (parking & transit)
- Cell phone stipend
- 401(k) Retirement plan with company match up to 4% of salary
- Volunteer time off
- Global travel insurance & emergency assistance
- Daily meals allowance
- Additional perks & programs specific to location
COMPENSATION RANGE
Compensation will be paid in the range of up to $185,000 - $225,000 + Bonus. Restricted Stock Units are included in all offers. Compensation to be determined by the applicant's knowledge, education, and abilities, as well as internal equity and alignment with market data.
Crusoe is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, disability, genetic information, pregnancy, citizenship, marital status, sex/gender, sexual preference/orientation, gender identity, age, veteran status, national origin, or any other status protected by law or regulation.
See all 284+ STEM OPT Applied AI Engineer Jobs
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Get Access To All JobsTips for Finding STEM OPT Authorization as an Applied AI Engineer
Confirm your CIP code qualifies first
Check that your degree's Classification of Instructional Programs code appears on the USCIS STEM Designated Degree Program List before applying. Computer science, electrical engineering, and data science CIP codes typically qualify, but applied mathematics or interdisciplinary programs sometimes don't.
Verify E-Verify enrollment before accepting offers
Search the E-Verify employer search tool to confirm any company is actively enrolled, not just registered. Enrollment status can lapse, and accepting an offer from an unenrolled employer invalidates your STEM OPT extension before it begins.
Frame your I-983 training plan around AI deliverables
Work with your DSO to list specific model training tasks, ML pipeline responsibilities, and evaluation benchmarks in your I-983. Vague descriptions like 'software development' create compliance risk during ICE site visits; AI-specific objectives tied to your degree field do not.
Target teams with dedicated ML infrastructure budgets
Applied AI Engineer roles at companies without production ML systems often convert to generalist software roles over time, which can undermine the degree-field connection USCIS requires. Look for teams running inference at scale or maintaining model registries as a sign of genuine AI practice.
Use Migrate Mate to filter for E-Verify employers hiring OPT students
Migrate Mate lets you search Applied AI Engineer openings filtered to employers already enrolled in E-Verify and with documented OPT hiring history, cutting the manual verification step out of your job search entirely.
Start your H-1B cap-gap timeline from day one
If your STEM OPT end date falls after April 1, file your H-1B visa registration before your OPT expires. A timely-filed H-1B petition triggers cap-gap protection, letting you keep working under the same employer without a gap in authorization.
Frequently Asked Questions
Does an Applied AI Engineer role qualify for the STEM OPT extension?
Yes, if your underlying degree carries a qualifying CIP code. Computer science, electrical engineering, statistics, and data science programs typically appear on the USCIS STEM Designated Degree Program List. The role itself also needs to directly relate to that degree field, so a job focused on building, training, or deploying AI models fits cleanly. Confirm your specific CIP code with your DSO before filing.
What E-Verify steps does my employer need to complete for my STEM OPT?
Your employer must be actively enrolled in E-Verify before your STEM OPT extension start date. Enrollment alone isn't enough; the company must also sign the I-983 training plan with you and your DSO, report material changes to your role within five business days, and cooperate with any ICE site visit. You can confirm active enrollment status through the E-Verify employer search tool before signing an offer.
What goes into the I-983 training plan for an Applied AI Engineer?
The I-983 must describe specific learning objectives tied to your degree field, not just your job title. For an Applied AI Engineer, that means listing concrete deliverables: model architecture decisions, dataset pipeline ownership, evaluation framework development, or deployment responsibilities. Generic software tasks weaken the connection to your STEM degree and create compliance exposure. Your DSO reviews and co-signs the plan before ICE and SEVP receive it.
How does cap-gap protection work if my STEM OPT expires during H-1B processing?
Cap-gap automatically extends your work authorization if your employer files a timely H-1B petition before your OPT EAD expires. The extension runs through September 30 of the fiscal year the H-1B is filed for. You continue working for the same E-Verify employer under the same role without interruption. USCIS issues a cap-gap receipt notice confirming your continued authorization.
Where can I find Applied AI Engineer jobs with employers already enrolled in E-Verify?
Migrate Mate filters Applied AI Engineer listings to employers enrolled in E-Verify and with a history of hiring F-1 OPT students, so you're not manually cross-referencing the E-Verify employer search against job postings. You can also check DOL Labor Condition Application disclosure data to see which companies have filed for AI-related roles previously, which is a reliable signal that their HR teams understand OPT documentation requirements.