Machine Learning Engineer Visa Sponsorship Jobs in Georgia
Machine learning engineer visa sponsorship jobs in Georgia are concentrated in Atlanta, where companies like Google, NCR Voyix, and Delta Air Lines run active ML teams. The broader metro also draws demand from fintech firms and Georgia Tech's deep talent pipeline, making it one of the Southeast's strongest markets for international ML candidates.
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Equifax is looking for a Machine Learning Engineer to join our Data and Analytics Center of Excellence (D&A COE). In this role, you will serve as the critical engineering bridge between advanced AI research and robust, scalable model development. Your primary mandate is to accelerate our R&D lifecycle by engineering high-performance training pipelines and unlocking model portability across the organization.
You will partner closely with applied researchers to translate proprietary deep learning architectures into clean, modular, and reusable codebases. By leveraging distributed computing and deep learning frameworks, you will help eliminate I/O bottlenecks, maximize GPU hardware utilization during training, and transform complex financial data into standardized, easily accessible formats. Your work will directly empower teams across the organization to efficiently train, scale, and iterate on some of the most complex tabular and time-series models in the financial sector.
Equifax has a hybrid work schedule that allows for two days of remote work (Monday and Friday) with 3 days onsite (Tuesday thru Thursday) every week.
This role reports to our office Alpharetta, GA and our Midtown (OAC, Atlanta) office may be considered.
This position does offer immigration sponsorship (current or future) including F-1 STEM OPT extension support.
This is a direct-hire role and is not open to C2C or vendors.
What you’ll do
- Design and build high-throughput data pipelines (e.g., BigQuery to TFRecords) specifically engineered for distributed training and inference.
- Partner with applied data scientists to translate complex prototypes into clean, modular, and scalable production-ready code.
- Productionize machine learning models by building performant data transformations, storage, and pipelines.
- Apply development and testing best practices and demonstrate skilled software craftsmanship to produce maintainable, scalable, and quality solutions.
- Contribute to all phases of product development and delivery from Analysis & Design all the way through to successful Deployment.
- Stay current on state-of-the-art deep learning architecture and training paradigms.
- Demonstrate effective, respectful, and honest communication when collaborating with colleagues including a cross-functional team consisting of QA, Operations, and other team members.
- Deliver on company initiatives and projects prioritized for your team and support long term technical vision.
What experience you need
- BS degree in a STEM major or equivalent job experience required; Master’s Degree preferred; AI/ML coursework preferred
- 2-5 years of related experience
- Proficiency in Python and experience with data processing and machine learning libraries (Pandas, Numpy, Scipy, Sklearn, Tensorflow, Pytorch, etc.)
- Experience with ML models design, development or deployment
- Experience with cloud platforms and distributed computing frameworks
- Experience writing complex SQL queries and building large-scale data transformation pipelines to feed machine learning workflows.
- Experience architecting deep learning systems (TensorFlow ecosystem preferred), including custom data loading pipelines (e.g., tf.data, TFRecord serialization).
- Cloud Certification Strongly Preferred
What could set you apart
- Advanced Framework Knowledge - Experience with deep learning framework (e.g. Tensorflow, Jax) internals, including custom training loops, subclassed Keras layers (e.g., custom attention mechanisms), and distributed training strategies (e.g., via tf.distribute).
- Large-Scale Distributed AI - Experience scaling models for distributed training and inference across multi-GPU clusters utilizing data, model, and/or tensor parallelism.
- Domain Expertise - Background in building models utilizing financial, credit, or complex time-series data
- Cloud Computing - Understand big data processing frameworks and various database technologies
- Mathematics - Understand advanced statistical concepts and machine learning algorithms
- Collaboration - Excellent verbal and written communication skills to document and present findings clearly
- Technical Leadership - Demonstrates an ability to provide guidance to colleagues
LI-AM2
LI-Hybrid
Job Description
Equifax is looking for a Machine Learning Engineer to join our Data and Analytics Center of Excellence (D&A COE). In this role, you will serve as the critical engineering bridge between advanced AI research and robust, scalable model development. Your primary mandate is to accelerate our R&D lifecycle by engineering high-performance training pipelines and unlocking model portability across the organization.
You will partner closely with applied researchers to translate proprietary deep learning architectures into clean, modular, and reusable codebases. By leveraging distributed computing and deep learning frameworks, you will help eliminate I/O bottlenecks, maximize GPU hardware utilization during training, and transform complex financial data into standardized, easily accessible formats. Your work will directly empower teams across the organization to efficiently train, scale, and iterate on some of the most complex tabular and time-series models in the financial sector.
Equifax has a hybrid work schedule that allows for two days of remote work (Monday and Friday) with 3 days onsite (Tuesday thru Thursday) every week.
This role reports to our office Alpharetta, GA and our Midtown (OAC, Atlanta) office may be considered.
This position does offer immigration sponsorship (current or future) including F-1 STEM OPT extension support.
This is a direct-hire role and is not open to C2C or vendors.
What you’ll do
- Design and build high-throughput data pipelines (e.g., BigQuery to TFRecords) specifically engineered for distributed training and inference.
- Partner with applied data scientists to translate complex prototypes into clean, modular, and scalable production-ready code.
- Productionize machine learning models by building performant data transformations, storage, and pipelines.
- Apply development and testing best practices and demonstrate skilled software craftsmanship to produce maintainable, scalable, and quality solutions.
- Contribute to all phases of product development and delivery from Analysis & Design all the way through to successful Deployment.
- Stay current on state-of-the-art deep learning architecture and training paradigms.
- Demonstrate effective, respectful, and honest communication when collaborating with colleagues including a cross-functional team consisting of QA, Operations, and other team members.
- Deliver on company initiatives and projects prioritized for your team and support long term technical vision.
What experience you need
- BS degree in a STEM major or equivalent job experience required; Master’s Degree preferred; AI/ML coursework preferred
- 2-5 years of related experience
- Proficiency in Python and experience with data processing and machine learning libraries (Pandas, Numpy, Scipy, Sklearn, Tensorflow, Pytorch, etc.)
- Experience with ML models design, development or deployment
- Experience with cloud platforms and distributed computing frameworks
- Experience writing complex SQL queries and building large-scale data transformation pipelines to feed machine learning workflows.
- Experience architecting deep learning systems (TensorFlow ecosystem preferred), including custom data loading pipelines (e.g., tf.data, TFRecord serialization).
- Cloud Certification Strongly Preferred
What could set you apart
- Advanced Framework Knowledge - Experience with deep learning framework (e.g. Tensorflow, Jax) internals, including custom training loops, subclassed Keras layers (e.g., custom attention mechanisms), and distributed training strategies (e.g., via tf.distribute).
- Large-Scale Distributed AI - Experience scaling models for distributed training and inference across multi-GPU clusters utilizing data, model, and/or tensor parallelism.
- Domain Expertise - Background in building models utilizing financial, credit, or complex time-series data
- Cloud Computing - Understand big data processing frameworks and various database technologies
- Mathematics - Understand advanced statistical concepts and machine learning algorithms
- Collaboration - Excellent verbal and written communication skills to document and present findings clearly
- Technical Leadership - Demonstrates an ability to provide guidance to colleagues
LI-AM2
LI-Hybrid
Machine Learning Engineer Job Roles in Georgia
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Search Machine Learning Engineer Jobs in GeorgiaMachine Learning Engineer Jobs in Georgia: Frequently Asked Questions
Which companies sponsor visas for machine learning engineers in Georgia?
Georgia's largest visa sponsors for machine learning engineers include Google's Atlanta office, NCR Voyix, Cox Enterprises, and Delta Air Lines, all of which have filed H-1B petitions for ML roles in recent years. Financial technology companies headquartered in Atlanta, such as Global Payments and Fiserv, also sponsor ML engineers regularly. Defense contractors near Warner Robins occasionally hire for ML roles with sponsorship as well.
Which visa types are most common for machine learning engineer roles in Georgia?
The H-1B is by far the most common visa for machine learning engineers in Georgia, as ML roles typically qualify as specialty occupations requiring at least a bachelor's degree in computer science, statistics, or a related field. Candidates with extraordinary ability may pursue the O-1A. International students at Georgia Tech or Emory finishing STEM degrees frequently use OPT or STEM OPT extension as a bridge before their employer files an H-1B petition.
Which cities in Georgia have the most machine learning engineer sponsorship jobs?
Atlanta accounts for the overwhelming majority of machine learning engineer sponsorship jobs in Georgia, particularly in the Midtown, Buckhead, and Peachtree Corners corridors where major tech and fintech offices cluster. Alpharetta and Dunwoody, both part of the greater Atlanta metro, host technology campuses that hire ML engineers with sponsorship. Outside metro Atlanta, opportunities are limited, though Augusta and Savannah have emerging tech presences.
How to find machine learning engineer visa sponsorship jobs in Georgia?
Migrate Mate is built specifically for international candidates and filters machine learning engineer roles in Georgia by visa sponsorship willingness, saving you from sorting through listings that won't support work authorization. You can search by job title and state to surface relevant openings at Georgia-based employers with a documented history of H-1B filings. Combining Migrate Mate with direct outreach to Atlanta's fintech and tech firms tends to produce the best results for ML candidates.
Are there any Georgia-specific factors that affect machine learning engineer sponsorship hiring?
Georgia Tech's machine learning and AI programs produce a large pool of OPT-eligible candidates, which means many Atlanta employers are already familiar with sponsorship processes for ML roles. The state's growing fintech and data analytics sectors create steady demand, particularly for engineers with experience in production ML systems. Georgia does not impose state-level restrictions on H-1B sponsorship, so federal prevailing wage requirements under the Department of Labor's LCA process are the primary compliance consideration employers navigate.
What is the prevailing wage for sponsored machine learning engineer jobs in Georgia?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.
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