Machine Learning Engineer Jobs in USA with Visa Sponsorship
Machine learning engineers who build the infrastructure to train, deploy, and monitor ML models at scale are critically needed by US companies operationalizing their data science investments. This role sits at the intersection of software engineering and data science - requiring expertise in feature engineering, model serving, distributed training, and monitoring - which makes it a strong specialty occupation for visa sponsorship. Employers ranging from FAANG to fintech to healthcare AI companies sponsor machine learning engineers because reliable ML infrastructure is what turns experimental models into revenue-generating products. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsOverview Of The Role
Citi, the leading global bank, has approximately 200 million customer accounts and does business in more than 160 countries and jurisdictions. Citi provides consumers, corporations, governments, and institutions with a broad range of financial products and services, including consumer banking and credit, corporate and investment banking, securities brokerage, transaction services, and wealth management. As a bank with a brain and a soul, Citi creates economic value that is systemically responsible and in our clients’ best interests. As a financial institution that touches every region of the world and every sector that shapes your daily life, our Enterprise Operations & Technology teams are charged with a mission that rivals any large tech company. Our technology solutions are the foundations of everything we do from keeping the bank safe, managing global resources, and providing the technical tools our workers need to be successful to designing our digital architecture and ensuring our platforms provide a first-class customer experience. We reimagine client and partner experiences to deliver excellence through secure, reliable, and efficient services. Our commitment to diversity includes a workforce that represents the clients we serve from all walks of life, backgrounds, and origins. We foster an environment where the best people want to work. We value and demand respect for others, promote individuals based on merit, and ensure opportunities for personal development are widely available to all. Ideal candidates are innovators with well-rounded backgrounds who bring their authentic selves to work and complement our culture of delivering results with pride. If you are a problem solver who seeks passion in your work, come join us. We’ll enable growth and progress together.
The Gen AI-ML Engineer is an intermediate level position responsible for participation in the establishment and implementation of new or revised application systems and programs in coordination with the Technology team. The overall objective of this role is to contribute to applications systems analysis and programming activities.
Responsibilities
- Design, develop, and implement GenAI solutions for various financial applications, including personalized recommendations, risk assessment, fraud detection, and automated reporting. Explore and experiment with advanced GenAI concepts like Agentic AI.
- Design and implement intelligent chatbots.
- Process and analyze large datasets of structured and unstructured financial data.
- Architect and implement efficient RAG pipelines, leveraging tools like LlamaIndex and LangChain.
- Develop and refine advanced prompting strategies for LLMs.
- Test, evaluate, and analyze the performance of LLM and other GenAI models.
- Collaborate closely with engineering teams to deploy and maintain GenAI models in production environments, including containerization, CI/CD pipelines, and cloud infrastructure management.
- Communicate effectively with business stakeholders.
- Stay up-to-date with the latest advancements in GenAI research and development, including areas like Agentic AI.
Required Skills And Qualifications
- 5 years+ of experience in AI/ML development, with a proven track record of building and deploying sophisticated GenAI applications.
- Deep understanding of GenAI models and architectures, including transformers, LLMs (e.g., Llama, Gemini, GPT-4), GANs, and diffusion models. Familiarity with Agentic AI concepts.
- Extensive experience with prompt engineering, fine-tuning LLMs, and evaluating their performance.
- Expert-level Python programming skills and proficiency with relevant libraries (e.g., Transformers, LangChain, TensorFlow, PyTorch, Pandas, NumPy, Scikit-learn, Flask/Django, LlamaIndex).
- Experience with vector databases (e.g., Pinecone, Weaviate, Chroma, Faiss, PostgreSQL with vector extensions) and implementing RAG pipelines using tools like LlamaIndex and LangChain.
- Strong software engineering skills, including containerization (Docker, Kubernetes), CI/CD pipelines, and cloud infrastructure management (AWS, Azure, GCP).
- Strong analytical, problem-solving, and communication skills.
- Experience with MLOps principles and tools.
- Excellent collaboration skills.
Technology Stack
- Programming Languages: Python (expert proficiency required)
- Python Packages: Transformers, LangChain, LlamaIndex, TensorFlow, PyTorch, Pandas, NumPy, Scikit-learn, Flask/Django, and other relevant data science, machine learning, and web development libraries.
- Deep Learning Frameworks: TensorFlow, PyTorch
- LLMs: Llama, Gemini, GPT-4, and other advanced LLMs.
- Vector Databases: Pinecone, Weaviate, Chroma, Faiss, PostgreSQL with vector extensions (pgvector).
- Cloud Platforms: AWS, Azure, GCP
- MLOps Tools: MLflow, Kubeflow, or similar.
- Containerization: Docker, Kubernetes
- CI/CD Tools: GitHub Actions, Jenkins, or similar.
- Version Control: Git
- Data Visualization & Reporting: Tableau, Power BI, matplotlib, seaborn.
- Databases: SQL and NoSQL databases.
Education:
Bachelor’s degree/University degree or equivalent experience
Job Family Group: Technology
Job Family: Applications Development
Time Type: Full time
Primary Location: Irving Texas United States
Primary Location Full Time Salary Range: $107,120.00 - $160,680.00
In addition to salary, Citi’s offerings may also include, for eligible employees, discretionary and formulaic incentive and retention awards. Citi offers competitive employee benefits, including: medical, dental & vision coverage; 401(k); life, accident, and disability insurance; and wellness programs. Citi also offers paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays. For additional information regarding Citi employee benefits, please visit citibenefits.com. Available offerings may vary by jurisdiction, job level, and date of hire.
Most Relevant Skills
Please see the requirements listed above.
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
Anticipated Posting Close Date: Jun 15, 2026
Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law. If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi. View Citi’s EEO Policy Statement and the Know Your Rights poster.
Overview Of The Role
Citi, the leading global bank, has approximately 200 million customer accounts and does business in more than 160 countries and jurisdictions. Citi provides consumers, corporations, governments, and institutions with a broad range of financial products and services, including consumer banking and credit, corporate and investment banking, securities brokerage, transaction services, and wealth management. As a bank with a brain and a soul, Citi creates economic value that is systemically responsible and in our clients’ best interests. As a financial institution that touches every region of the world and every sector that shapes your daily life, our Enterprise Operations & Technology teams are charged with a mission that rivals any large tech company. Our technology solutions are the foundations of everything we do from keeping the bank safe, managing global resources, and providing the technical tools our workers need to be successful to designing our digital architecture and ensuring our platforms provide a first-class customer experience. We reimagine client and partner experiences to deliver excellence through secure, reliable, and efficient services. Our commitment to diversity includes a workforce that represents the clients we serve from all walks of life, backgrounds, and origins. We foster an environment where the best people want to work. We value and demand respect for others, promote individuals based on merit, and ensure opportunities for personal development are widely available to all. Ideal candidates are innovators with well-rounded backgrounds who bring their authentic selves to work and complement our culture of delivering results with pride. If you are a problem solver who seeks passion in your work, come join us. We’ll enable growth and progress together.
The Gen AI-ML Engineer is an intermediate level position responsible for participation in the establishment and implementation of new or revised application systems and programs in coordination with the Technology team. The overall objective of this role is to contribute to applications systems analysis and programming activities.
Responsibilities
- Design, develop, and implement GenAI solutions for various financial applications, including personalized recommendations, risk assessment, fraud detection, and automated reporting. Explore and experiment with advanced GenAI concepts like Agentic AI.
- Design and implement intelligent chatbots.
- Process and analyze large datasets of structured and unstructured financial data.
- Architect and implement efficient RAG pipelines, leveraging tools like LlamaIndex and LangChain.
- Develop and refine advanced prompting strategies for LLMs.
- Test, evaluate, and analyze the performance of LLM and other GenAI models.
- Collaborate closely with engineering teams to deploy and maintain GenAI models in production environments, including containerization, CI/CD pipelines, and cloud infrastructure management.
- Communicate effectively with business stakeholders.
- Stay up-to-date with the latest advancements in GenAI research and development, including areas like Agentic AI.
Required Skills And Qualifications
- 5 years+ of experience in AI/ML development, with a proven track record of building and deploying sophisticated GenAI applications.
- Deep understanding of GenAI models and architectures, including transformers, LLMs (e.g., Llama, Gemini, GPT-4), GANs, and diffusion models. Familiarity with Agentic AI concepts.
- Extensive experience with prompt engineering, fine-tuning LLMs, and evaluating their performance.
- Expert-level Python programming skills and proficiency with relevant libraries (e.g., Transformers, LangChain, TensorFlow, PyTorch, Pandas, NumPy, Scikit-learn, Flask/Django, LlamaIndex).
- Experience with vector databases (e.g., Pinecone, Weaviate, Chroma, Faiss, PostgreSQL with vector extensions) and implementing RAG pipelines using tools like LlamaIndex and LangChain.
- Strong software engineering skills, including containerization (Docker, Kubernetes), CI/CD pipelines, and cloud infrastructure management (AWS, Azure, GCP).
- Strong analytical, problem-solving, and communication skills.
- Experience with MLOps principles and tools.
- Excellent collaboration skills.
Technology Stack
- Programming Languages: Python (expert proficiency required)
- Python Packages: Transformers, LangChain, LlamaIndex, TensorFlow, PyTorch, Pandas, NumPy, Scikit-learn, Flask/Django, and other relevant data science, machine learning, and web development libraries.
- Deep Learning Frameworks: TensorFlow, PyTorch
- LLMs: Llama, Gemini, GPT-4, and other advanced LLMs.
- Vector Databases: Pinecone, Weaviate, Chroma, Faiss, PostgreSQL with vector extensions (pgvector).
- Cloud Platforms: AWS, Azure, GCP
- MLOps Tools: MLflow, Kubeflow, or similar.
- Containerization: Docker, Kubernetes
- CI/CD Tools: GitHub Actions, Jenkins, or similar.
- Version Control: Git
- Data Visualization & Reporting: Tableau, Power BI, matplotlib, seaborn.
- Databases: SQL and NoSQL databases.
Education:
Bachelor’s degree/University degree or equivalent experience
Job Family Group: Technology
Job Family: Applications Development
Time Type: Full time
Primary Location: Irving Texas United States
Primary Location Full Time Salary Range: $107,120.00 - $160,680.00
In addition to salary, Citi’s offerings may also include, for eligible employees, discretionary and formulaic incentive and retention awards. Citi offers competitive employee benefits, including: medical, dental & vision coverage; 401(k); life, accident, and disability insurance; and wellness programs. Citi also offers paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays. For additional information regarding Citi employee benefits, please visit citibenefits.com. Available offerings may vary by jurisdiction, job level, and date of hire.
Most Relevant Skills
Please see the requirements listed above.
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
Anticipated Posting Close Date: Jun 15, 2026
Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law. If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi. View Citi’s EEO Policy Statement and the Know Your Rights poster.
See all 4,253+ Machine Learning Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Engineer roles.
Get Access To All JobsTips for Finding Visa Sponsorship as a Machine Learning Engineer
Emphasize production engineering over research
MLE roles focus on deploying, scaling, and monitoring models in production - not just training them. Highlight experience with model serving frameworks like TensorFlow Serving, TorchServe, or Triton Inference Server to stand out.
Target companies with mature ML infrastructure teams
Google, Meta, Netflix, Uber, and Spotify have dedicated MLE teams that build and maintain production ML systems. These companies sponsor H-1B petitions under SOC 15-1252 and understand the engineering nature of the role.
Leverage your dual skill set in interviews
The MLE role bridges data science and software engineering, and that's your selling point. Strong candidates can discuss both model optimization and system design, which is rare and makes employers more willing to invest in sponsorship.
Build MLOps expertise to increase your value
Feature stores, experiment tracking, model monitoring, and automated retraining pipelines are critical MLE skills. Companies building serious ML products need engineers who can operationalize models, not just build prototypes.
Use STEM OPT to prove production reliability
With a STEM-eligible degree, you get up to 3 years of work authorization through OPT. ML systems require deep institutional knowledge to maintain - use that time to become indispensable to your team's production stack.
File under the right SOC code for engineering
MLE roles typically file under SOC 15-1252 (Software Developers), emphasizing the engineering and systems side of the work. This classification has strong precedent for H-1B approval - ensure your job description reflects the production engineering focus.
Frequently Asked Questions
What ML infrastructure skills are most valued by employers sponsoring machine learning engineers?
Experience with distributed training frameworks (PyTorch Distributed, DeepSpeed), model serving platforms (TensorFlow Serving, NVIDIA Triton, ONNX Runtime), and feature engineering tools (Feast, Tecton) are the most sought-after skills. Knowledge of GPU cluster management, inference cost optimization, and monitoring for data drift also carries significant weight. These specific technical requirements are exactly what make the visa petition strong, because they show the role requires specialized knowledge beyond general software engineering.
Do machine learning engineers need a PhD, or is a master's degree sufficient for sponsorship?
A master's degree is sufficient for the vast majority of ML engineering roles, and many positions only require a bachelor's in computer science or a related field. A PhD is more commonly expected for research-focused ML positions, not engineering roles focused on production systems. That said, a master's degree qualifies you for the additional 20,000 H-1B visa cap exemption slots reserved for U.S. advanced degree holders, which improves your lottery odds.
I have a research background but want to move into ML engineering. How does this affect sponsorship?
The transition is common and does not create visa issues. Your research background demonstrates the theoretical knowledge needed to make sound infrastructure decisions, while any production-adjacent work from your research (deploying models, building data pipelines, optimizing training runs) shows practical engineering capability. If you have a PhD, you benefit from the advanced degree H-1B exemption. The combination of theoretical depth from research and hands-on engineering skills can actually strengthen your petition.
How to find Machine Learning Engineer jobs with visa sponsorship?
To find Machine Learning Engineer jobs with visa sponsorship, use Migrate Mate, which specializes in connecting international talent with sponsoring employers. Focus on tech companies, startups, and research institutions that commonly hire ML engineers on H-1B, O-1 visa, or other work visas. These employers often need specialized AI/ML expertise and are willing to sponsor qualified candidates with relevant experience in data science, neural networks, and algorithm development.
Which companies sponsor machine learning engineers most actively?
Companies operationalizing ML at scale are the most active sponsors. This includes large tech firms (Google, Meta, Amazon, Microsoft), ML-first product companies (Spotify, Netflix, Uber, Stripe), and AI infrastructure startups (Databricks, Anyscale, Weights & Biases). Fintech and healthcare AI companies are also growing sponsors. Look for employers whose products depend on reliable ML systems in production, as they are most motivated to invest in sponsorship for engineers who can bridge the gap between a trained model and a live product.
What prevailing wage levels typically apply to ML engineering roles?
ML engineering salaries typically place candidates at Level 3 or Level 4 of the Department of Labor prevailing wage system, which is favorable for visa petitions. Higher wage levels signal to USCIS that the role is senior and specialized, reducing the risk of a Request for Evidence. If an employer offers a salary at Level 1, that is a red flag for both immigration risk and fair compensation. You can check prevailing wages for your role and location on the DOL's Foreign Labor Certification Data Center.
What is the prevailing wage requirement for sponsored Machine Learning Engineer jobs?
When a U.S. employer sponsors a foreign worker for a work visa, they are legally required to pay at least the "prevailing wage", the average wage paid to workers in the same occupation, in the same geographic area, with similar experience. This is set by the Department of Labor to prevent employers from hiring foreign workers at below-market rates. The prevailing wage varies significantly by role, location, and experience level. For example, a machine learning engineer in California will have a different prevailing wage than the same role in a smaller state. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search Page.