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 JobsINTRODUCTION
Children’s is one of the nation’s leading children’s hospitals. No matter the role, every member of our team is an essential part of our mission to make kids better today and healthier tomorrow. We’re committed to putting you first, and that commitment is at the heart of our company culture: People first. Children always. Find your next career opportunity and make a difference doing what you love at Children’s.
ROLE AND RESPONSIBILITIES
The Data Science Intern must have ability to perform exploratory data analyses, create effective data visualizations and have exposure to the theory and application of predictive (machine learning) and/or inferential (classical statistics) methods. As part of this internship, they will work side by side with full time data scientists, ML Engineer, business intelligence and reporting analysts to complete an internship project that may have a clinical, financial or operational focus. A final project presentation will be given to department leadership.
Example projects include:
- Python / ML focus (primary): Support the development and operationalization of predictive models at various lifecycle stages (e.g., exploratory data analyses for proposed features or prediction targets, development of model training, evaluation, and monitoring pipelines etc.), with a focus on predictive models for capacity management that use advanced time series forecasting methods.
- R / stats focus: Enhance advanced analytics dashboards and applications (e.g., SPC ChartR, BMH Recidivism), with a focus on hardening existing R package development for automation of statistical process control charts.
Goals:
- Produce a cohesive proof of concept that meet system and department goals
- Gain exposure to the tools, concepts, and practices of advanced analytics in the context of a large healthcare provider
Tasks:
- Extract, transform, process and analyze complex data sets using appropriate tools
- Follow technical guidelines and best practices for project management, source control, documentation and responsible machine learning
- Effectively collaborate with team members and stakeholders across the health system
- Communicate results and provide recommendations for improving data and/or operational workflows
- Follow department and team norms for agile work management (daily standups, sprint planning etc.)
- Contribute to organizational knowledge by participating in internal knowledge shares/seminars
The Children's Intern program allows interns the opportunity to gain hands-on experience related to their field of study by working on meaningful projects alongside Children’s professionals. Intern responsibilities may include project management, event planning and support, logistics, data base management, research, and analysis. Interns may explore career paths and apply for full-time positions upon successful completion of the program.
BASIC QUALIFICATIONS
- Research area: research experience necessary either through previous internship, work experience, or course work; practical knowledge about the conduct of research principals required
PREFERRED QUALIFICATIONS
- Progression toward an undergraduate or graduate degree (preferred) in Biostatistics, Health Informatics, Public/Behavioral health, Epidemiology, Statistics, Data Science, Analytics, Machine Learning, Information Systems, Bioinformatics, Computer Science, Industrial Engineering)
- Experience with advanced analytics tools (ex R, Python) and/or SQL data extraction and manipulation (ex: Oracle, Microsoft SQL Server) in a research, academic or business context
- Knowledge of at least one of the following: Machine learning algorithms (ex: unsupervised clustering, logistic regression, XGBoost, lightgbm, random forest, neural networks); Classical statistics methods (ex: bivariate analysis, regression analysis, ANOVA, survival models, principal component analysis, forecasting); and/or RShiny, Plotly or equivalent data visualization and dashboarding tools (ex: Qlik, Tableau, Power BI)
- Familiarity with at least some of the following: Use of integrated development environment (e.g., RStudio Workbench, Pycharm, Visual Studio) and notebook tools (Rmarkdown, Jupyter) to create and share reproducible analyses; Version control and issue tracking tools (ex: Azure DevOps, Gitlab, Github); Cloud computing and big data platforms (ex: Snowflake, Spark, SCALA, Databricks, Azure ML); Natural language processing and text analytics tools (ex: NLTK, Word2Vec, spaCy), anomaly detection techniques (ex: isolation forests, k-nearest neighbor) and real-time data streaming engines (ex: Kafka, Flume, Spark)
Education
Clinical Focus:
- College student with at least two years in a health sciences related program, such as pre-med, nursing, biomedical engineering, biology, chemistry, or statistics, or post graduate student working toward a Master’s of Science in public health or medical degree
Non-Clinical Focus:
- College or graduate student that is currently working towards a Bachelor or Master’s degree in Journalism, Communications, Business, Marketing, Healthcare Administration or other related field, required
CERTIFICATION SUMMARY
- No professional certifications required
KNOWLEDGE, SKILLS, AND ABILITIES
- Organized, detail oriented; Able to prioritize time sensitive assignments
- Creative and flexible; Able to adapt to change
- Self-starter; Able to make decisions independently
- Strong verbal and written communication skills; Strong interpersonal and presentation skills
- Able to work well with diverse groups, comfortable interacting with all levels
- Able to represent Children’s in a mature and professional manner
- Willing to work long hours that could include evenings and weekends, if applicable to internship
- Proficient with Microsoft Office applications (Word, Excel, Power Point, Access, Outlook) or other applications as required
- Able to travel throughout expanded metro Atlanta area; Must provide reliable transportation, if applicable to internship
Clinical Focus:
- Knowledge of medical terminology useful
- Knowledge of basic statistical software useful
JOB RESPONSIBILITIES
- Develops and implements projects as assigned, which could include events, activities, programs, or research studies.
- Creates and carries out a cohesive plan for each assigned project. Establishes and maintains contact with all appropriate individuals to ensure that the plan is implemented in the best interest of the organization.
- Executes administrative and operational tasks for assigned projects.
- Supports and participates in the continuous assessment and improvement of the quality of services provided and projects produced.
- Understands and complies with infection control, safety, and OSHA procedures and regulations, while meeting all in-service requirements as outlined per policy.
LOCATION
1575 Northeast Expy NE
WORK SHIFT
Day
WORK DAY(S)
Friday, Monday, Thursday, Tuesday, Wednesday
SHIFT START TIME
8:00 AM
SHIFT END TIME
5:00 PM
WORKER SUB-Type
Temporary
Children’s Healthcare of Atlanta is an equal opportunity employer committed to providing equal employment opportunities to all qualified applicants and employees without regard to race, color, sex, religion, national origin, citizenship, age, veteran status, disability or any other characteristic covered by applicable law.
How to Get 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.
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Get Access To All JobsFrequently 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 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.
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.
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