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.
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Get Access To All JobsINTRODUCTION
A Career with Point72’s Technology Team As Point72 reimagines the future of investing, our Technology team is constantly evolving our firm’s IT infrastructure and engineering capabilities, positioning us at the forefront of a rapidly evolving technology landscape. We’re a team of experts who experiment and work to discover new ways to harness open-source solutions, modern cloud architectures, and sophisticated Artificial Intelligence (AI) solutions, while embracing enterprise agile methodologies. Our commitment to building and innovating in the AI space provides the framework intended to drive smarter decision making and enhance how we build and operate our platforms and applications. As a member of Point72’s Technology team, we encourage and support your professional development from day one—helping you advance your technical skills, contribute innovative ideas, and satisfy your own intellectual curiosity—all while delivering real business impact for our multi-billion-dollar global business.
ROLE AND RESPONSIBILITIES
What you’ll do
Develop and maintain scalable artificial intelligence/machine learning (AI/ML) architectures and systems.
Collaborate with data scientists, engineers, product teams, and Compliance to integrate AI/ML solutions into existing and new products.
Evaluate tools, technologies, and processes to ensure the highest quality and performance of AI/ML systems.
Stay abreast of the latest advancements in AI/ML technologies and methodologies.
* Ensure compliance with industry standards and best practices in AI/ML.
BASIC QUALIFICATIONS
Bachelor's or master's degree in computer science, engineering, or a related field.
3-7 years of experience in AI/ML engineering, with a proven track record of successful project delivery.
Strong expertise in machine learning frameworks and libraries, such as TensorFlow, PyTorch, and/or Scikit-learn.
Proficiency in programming languages such as Python, Java, or C++.
Excellent problem-solving skills with the ability to work independently and collaboratively.
Strong communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
* Commitment to the highest ethical standards.
PREFERRED QUALIFICATIONS
We take care of our people
We invest in our people, their careers, their health, and their well-being. When you work here, we provide:
Fully-paid health care benefits
Generous parental and family leave policies
Mental and physical wellness programs
Volunteer opportunities
Non-profit matching gift program
Support for employee-led affinity groups representing women, minorities and the LGBT+ community
Tuition assistance
A 401(k) savings program with an employer match and more
ABOUT POINT72
Point72 Asset Management is a global firm led by Steven Cohen that invests in multiple asset classes and strategies worldwide. Resting on more than a quarter-century of investing experience, we seek to be the industry’s premier asset manager through delivering superior risk-adjusted returns, adhering to the highest ethical standards, and offering the greatest opportunities to the industry’s brightest talent. We’re inventing the future of finance by revolutionizing how we develop our people and how we use data to shape our thinking.
The annual base salary range for this role is $185,000-$300,000 (USD), which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.
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.
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