Green Card AI ML Platform Jobs
AI ML Platform roles qualify for EB-2 sponsorship when the position requires an advanced degree in machine learning, computer science, or a related field, and for EB-3 when a bachelor's degree meets the specialty occupation standard. Your employer files a PERM labor certification with the DOL before the I-140 petition, permanently tying your green card path to the role.
Find Green Card AI ML Platform JobsOverview
Showing 5 of 576+ AI ML Platform jobs
See all 576+ AI ML Platform Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new AI ML Platform roles.
Get Access To All JobsAbout Egen:
Egen is a fast-growing and entrepreneurial company with a data-first mindset. We bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights. We are committed to being a place where the best people choose to work so they can apply their engineering and technology expertise to envision what is next for how data and platforms can change the world for the better. We are dedicated to learning, thrive on solving tough problems, and continually innovate to achieve fast, effective results. If this describes you, we want you on our team.
Want to learn more about life at Egen? Check out these resources in addition to the job description.
Meet Egen
Life at Egen
Culture and Values at Egen
Career Development at Egen
Benefits at Egen
About the opportunity
We're looking for an engineer who is equally at home running a GPU training platform and the large-scale model training that runs on it. You'll be embedded with a healthcare and life-sciences AI client that trains foundation models on a large GKE-based H200 and GB200 cluster. The client is reorganizing so that its foundation-model platform and its agent platform teams work more closely together.
The role centres on the training stack and the scientists who use it. Solid infrastructure knowledge and experience are required, but the client already has strong SRE and infrastructure capacity, so running the cluster isn't the focus: you'll partner with that team and understand the training workloads well enough to tune, benchmark, and debug them end to end. You'll work alongside other Egen engineers and client scientists, and you'll own your area outright.
What You Will Do:
Training platform
- Work with the client's infrastructure team on the GKE training platform (GPU node pools, job queueing and scheduling, quota and capacity) from the training workload's side.
- Diagnose and fix platform failures at the root (for example a controller that keeps running out of memory) rather than restarting them.
- Tune multi-node GPU networking (RDMA/GPUDirect, NCCL) for training throughput.
Distributed training
- Run, scale and debug multi-node distributed training with PyTorch (FSDP, DeepSpeed, Megatron or NeMo-style) and Ray Train.
- Profile jobs and remove bottlenecks across compute, memory, input pipelines and interconnect.
Benchmarking and evals
- Design and run performance benchmarks for the cluster and training stack: throughput, scaling efficiency, cost per run.
- Build and maintain model evaluation harnesses so changes to the platform or training recipes are measured, not assumed.
- Instrument training and the platform with telemetry and monitoring, and keep end-to-end pipeline tests running against what users actually run.
Ownership on the client team
- Treat the platform as your own: proactively find issues, plug gaps and propose solutions before anyone asks, keep the team informed of status and risks, and look after the scientists who use it.
- Explain and defend your design decisions to client engineers and scientists in detail.
- Stay effective while the client's priorities and team structure change.
Your Technical Toolkit:
- Core Languages: Mastery of Python and shell scripting
- AI/LLM Ecosystem: Extensive experience with Google Gemini, GPT-4, or LLaMA; deep knowledge of Prompt Engineering and Fine-tuning
- Data & Search: Expertise in Vector Databases (Vertex AI Vector Search, pgvector, etc.) and implementing Semantic Search
- Infrastructure: Hands-on experience with Google Cloud (Vertex AI) and building scalable software architectures
- Frameworks: Proficiency in LangChain, LlamaIndex, or similar orchestration layers
- Mindset: A strong software engineering foundation—you write clean, maintainable code and understand the full SDLC
Basic Qualifications:
- 5+ years of engineering experience, with recent hands-on work on GPU training infrastructure and large-scale training.
- Strong Python software engineering: production-quality code, tests and code review. Coding is central to this role, not an afterthought.
- GKE and GPU scheduling: Kubernetes on Google Cloud with GPU node pools, job queueing (Kueue or similar), and capacity and quota management.
- Distributed PyTorch and Ray Train: has personally run and debugged multi-node training jobs.
- RDMA and GPU networking: GPUDirect/RDMA, NCCL tuning, multi-node GB200 (or H200) topology.
- Benchmarking and evals: has designed performance benchmarks and model evaluations, and used them to make decisions.
- Deep enough understanding to explain how and why a system works, without leaning on AI coding tools to carry the reasoning.
- A track record of visible ownership: present, communicative, and accountable on a demanding team.
Personal Attributes:
Nice to have
- Healthcare or life-sciences data (genomics, clinical, biomedical), and working under HIPAA.
- Observability and telemetry for training platforms: metrics, tracing, alerting, end-to-end pipeline testing.
Compensation
- Salary Range: $159,300 - $219,245 a year
This position may be hired at multiple levels. Final leveling is determined during the interview process based on a candidate's experience, skills, and interview outcomes, and the applicable salary range will align with the final level assigned.
Compensation is determined based on factors including experience, expertise, interview performance, and geographic location, where applicable.
Compensation & Benefits:
This role is eligible for our competitive salary and comprehensive benefits package to support your well-being:
- Comprehensive Health Insurance
- Paid Leave (Vacation/PTO)
- Paid Holidays
- Sick Leave
- Parental Leave
- Bereavement Leave
- 401 (k) Employer Match
- Employee Referral Bonuses
Check out our complete list of benefits here.
Important: All roles are subject to standard hiring verification practices, which may include background checks, employment verification, and other relevant checks.
EEO and Accommodations:
Egen is an equal opportunity employer and is committed to inclusion, diversity, and equity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veterans’ status, or any other characteristic protected by federal, state, or local laws. Egen will also consider qualified applications with criminal histories, consistent with legal requirements. Egen welcomes and encourages applications from individuals with disabilities. Reasonable accommodations are available for candidates during all aspects of the selection process. Please advise the talent acquisition team if you require accommodations during the interview process.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. 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 Egen:
Egen is a fast-growing and entrepreneurial company with a data-first mindset. We bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights. We are committed to being a place where the best people choose to work so they can apply their engineering and technology expertise to envision what is next for how data and platforms can change the world for the better. We are dedicated to learning, thrive on solving tough problems, and continually innovate to achieve fast, effective results. If this describes you, we want you on our team.
Want to learn more about life at Egen? Check out these resources in addition to the job description.
Meet Egen
Life at Egen
Culture and Values at Egen
Career Development at Egen
Benefits at Egen
About the opportunity
We're looking for an engineer who is equally at home running a GPU training platform and the large-scale model training that runs on it. You'll be embedded with a healthcare and life-sciences AI client that trains foundation models on a large GKE-based H200 and GB200 cluster. The client is reorganizing so that its foundation-model platform and its agent platform teams work more closely together.
The role centres on the training stack and the scientists who use it. Solid infrastructure knowledge and experience are required, but the client already has strong SRE and infrastructure capacity, so running the cluster isn't the focus: you'll partner with that team and understand the training workloads well enough to tune, benchmark, and debug them end to end. You'll work alongside other Egen engineers and client scientists, and you'll own your area outright.
What You Will Do:
Training platform
- Work with the client's infrastructure team on the GKE training platform (GPU node pools, job queueing and scheduling, quota and capacity) from the training workload's side.
- Diagnose and fix platform failures at the root (for example a controller that keeps running out of memory) rather than restarting them.
- Tune multi-node GPU networking (RDMA/GPUDirect, NCCL) for training throughput.
Distributed training
- Run, scale and debug multi-node distributed training with PyTorch (FSDP, DeepSpeed, Megatron or NeMo-style) and Ray Train.
- Profile jobs and remove bottlenecks across compute, memory, input pipelines and interconnect.
Benchmarking and evals
- Design and run performance benchmarks for the cluster and training stack: throughput, scaling efficiency, cost per run.
- Build and maintain model evaluation harnesses so changes to the platform or training recipes are measured, not assumed.
- Instrument training and the platform with telemetry and monitoring, and keep end-to-end pipeline tests running against what users actually run.
Ownership on the client team
- Treat the platform as your own: proactively find issues, plug gaps and propose solutions before anyone asks, keep the team informed of status and risks, and look after the scientists who use it.
- Explain and defend your design decisions to client engineers and scientists in detail.
- Stay effective while the client's priorities and team structure change.
Your Technical Toolkit:
- Core Languages: Mastery of Python and shell scripting
- AI/LLM Ecosystem: Extensive experience with Google Gemini, GPT-4, or LLaMA; deep knowledge of Prompt Engineering and Fine-tuning
- Data & Search: Expertise in Vector Databases (Vertex AI Vector Search, pgvector, etc.) and implementing Semantic Search
- Infrastructure: Hands-on experience with Google Cloud (Vertex AI) and building scalable software architectures
- Frameworks: Proficiency in LangChain, LlamaIndex, or similar orchestration layers
- Mindset: A strong software engineering foundation—you write clean, maintainable code and understand the full SDLC
Basic Qualifications:
- 5+ years of engineering experience, with recent hands-on work on GPU training infrastructure and large-scale training.
- Strong Python software engineering: production-quality code, tests and code review. Coding is central to this role, not an afterthought.
- GKE and GPU scheduling: Kubernetes on Google Cloud with GPU node pools, job queueing (Kueue or similar), and capacity and quota management.
- Distributed PyTorch and Ray Train: has personally run and debugged multi-node training jobs.
- RDMA and GPU networking: GPUDirect/RDMA, NCCL tuning, multi-node GB200 (or H200) topology.
- Benchmarking and evals: has designed performance benchmarks and model evaluations, and used them to make decisions.
- Deep enough understanding to explain how and why a system works, without leaning on AI coding tools to carry the reasoning.
- A track record of visible ownership: present, communicative, and accountable on a demanding team.
Personal Attributes:
Nice to have
- Healthcare or life-sciences data (genomics, clinical, biomedical), and working under HIPAA.
- Observability and telemetry for training platforms: metrics, tracing, alerting, end-to-end pipeline testing.
Compensation
- Salary Range: $159,300 - $219,245 a year
This position may be hired at multiple levels. Final leveling is determined during the interview process based on a candidate's experience, skills, and interview outcomes, and the applicable salary range will align with the final level assigned.
Compensation is determined based on factors including experience, expertise, interview performance, and geographic location, where applicable.
Compensation & Benefits:
This role is eligible for our competitive salary and comprehensive benefits package to support your well-being:
- Comprehensive Health Insurance
- Paid Leave (Vacation/PTO)
- Paid Holidays
- Sick Leave
- Parental Leave
- Bereavement Leave
- 401 (k) Employer Match
- Employee Referral Bonuses
Check out our complete list of benefits here.
Important: All roles are subject to standard hiring verification practices, which may include background checks, employment verification, and other relevant checks.
EEO and Accommodations:
Egen is an equal opportunity employer and is committed to inclusion, diversity, and equity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veterans’ status, or any other characteristic protected by federal, state, or local laws. Egen will also consider qualified applications with criminal histories, consistent with legal requirements. Egen welcomes and encourages applications from individuals with disabilities. Reasonable accommodations are available for candidates during all aspects of the selection process. Please advise the talent acquisition team if you require accommodations during the interview process.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. 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 576+ Green Card AI ML Platform Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Green Card AI ML Platform Jobs.
Get Access To All JobsTips for Finding Green Card Sponsorship in AI ML Platform
Document your ML specialization for PERM
PERM requires your employer to prove the job genuinely requires your specific credentials. Gather transcripts, published research, and project documentation that link your advanced degree or specialized training directly to the AI ML Platform role's stated requirements.
Target employers with active I-140 filing history
Companies that have filed I-140 petitions for ML engineers in the past are already familiar with PERM requirements for technical roles. Prioritize these employers early, since their HR and legal teams won't need to be educated on the process from scratch.
Verify your SOC code before the offer stage
AI ML Platform roles typically fall under SOC 15-2051 or 15-1212. The SOC code your employer selects drives the prevailing wage determination from the OFLC Wage Search, so confirm it matches your actual duties before the job order is posted.
Negotiate the PERM timeline into your offer
PERM labor certification can take 12 to 18 months before the I-140 is even filed. Ask during offer negotiations whether the employer will start PERM immediately or delay, since that decision directly affects when your priority date is established.
Use Migrate Mate to filter for green card sponsoring roles
Search Migrate Mate to find AI ML Platform positions at employers with confirmed EB-2 and EB-3 sponsorship history, so you're applying to companies already set up to run PERM rather than ones that have never filed before.
Cross-check your O*NET profile against your job description
USCIS scrutinizes whether your duties match the specialty occupation definition. Pull the O*NET profile for your role's SOC code and confirm your offer letter's responsibilities align, since mismatches between the two are a common source of RFEs on I-140 petitions.
Green Card AI ML Platform: Frequently Asked Questions
Do AI ML Platform roles qualify for EB-2 or EB-3 green card sponsorship?
Most AI ML Platform positions qualify for EB-2 because they require an advanced degree in machine learning, computer science, statistics, or a closely related field. If the role requires only a bachelor's degree, it can qualify under EB-3 as a professional position. The classification affects your priority date queue but not your eligibility for PERM labor certification, which is required under both categories.
How does green card sponsorship for this role differ from H-1B sponsorship?
Green card sponsorship through PERM is a permanent immigration pathway, not a temporary status renewal. There is no annual lottery at the EB-3 level, and your employer files a labor certification with the DOL rather than competing for a cap slot. The process takes longer, often two to four years from PERM filing to an approved I-485, but the outcome is lawful permanent residency rather than a visa tied to your employer's continued sponsorship.
What does the PERM labor certification process look like for AI ML Platform positions?
Your employer must conduct a supervised recruitment campaign to demonstrate no qualified U.S. worker is available for the role. For AI ML Platform jobs, the DOL will assign a prevailing wage based on your SOC code and work location. The employer posts the position, collects and reviews applications, documents the results, and files ETA Form 9089. Only after DOL certifies that form does your employer file the I-140 petition with USCIS.
How do I find employers that will actually sponsor a green card for an AI ML Platform role?
Search Migrate Mate to identify companies with a documented history of EB-2 and EB-3 sponsorship for ML and platform engineering roles. Many employers in this space have sponsored before but don't advertise it in job postings. Filtering by sponsorship history means you start conversations with teams whose legal and HR processes are already built for PERM rather than negotiating from zero.
Can my employer start PERM while I'm on H-1B status, and what happens if my H-1B expires during the process?
Yes, your employer can file PERM while you're in valid H-1B visa status, and this is the standard path for most sponsored professionals in ML roles. If your H-1B expires before your green card is approved, you can extend it in three-year increments under AC21 portability rules once your I-140 is approved and your priority date is within one year of becoming current.