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 JobsOur Purpose
At SentinelOne, we are driven by a clear purpose: to give the advantage to those who secure our future. As AI reshapes how organizations build, operate, and innovate, the responsibility to protect them becomes more critical than ever. When you join SentinelOne, your work helps protect global enterprises, critical infrastructure, and the technologies shaping tomorrow. If you are motivated by meaningful challenges and want your impact to be real, measurable, and global, you will find purpose here.
About us
SentinelOne is a company at the intersection of AI and security, pioneering a new operating model for cybersecurity. Our AI-native platform unifies protection across endpoint, cloud, identity, data, and AI systems to deliver autonomous detection and response with clarity and speed. By combining real-time analytics, intelligent automation, and a unified data foundation, we reduce noise, simplify complexity, and empower security teams to focus on what truly matters.
Our teams are builders, problem-solvers, and innovators committed to shaping the future of security. If you are excited to solve hard problems alongside talented, mission-driven people, we invite you to help us build a safer future for humanity.
What Are We Looking For?
We're looking for people who are relentlessly curious and committed to continuous learning. AI is reshaping every function across our business, and we enable every team member, regardless of role or level, to build fluency in AI tools and concepts. Those who thrive here actively seek out new solutions, experiment thoughtfully, and apply what they learn to drive better, faster, smarter outcomes.
As a motivated PhD student with strong backend fundamentals who is excited about building production AI systems. This role is a great fit for an aspiring software engineer who works comfortably in AI-driven problem spaces and wants to apply software engineering rigor to create LLM-backed products and platforms.
This is not a research-only role. While we work closely with research and science teams, this position sits squarely in a product engineering organization. The focus is on designing and building reliable systems that ship real value to customers and internal users. We value curiosity, experimentation, and a commitment to continuous learning.
What will you do?
As an AI Software Engineering Intern, you will own an end-to-end project from idea to functioning prototype, with a clear path to production. You will:
- Develop Backend Services: Design and build services in Python that power AI-driven products and shared capabilities.
- Integrate Systems: Build resilient service integrations across internal systems, handling failure modes and rate limits.
- Build AI Features: Develop and evolve LLM-backed features and agentic workflows, focusing on reliability and real-world behavior.
- Collaborate Cross-Functionally: Work with product managers, researchers, and senior engineers to turn loosely defined AI use cases into concrete, production-ready systems.
- Shape AI Quality: Help build or extend evaluation harnesses, benchmarks, or feedback loops for AI-powered features.
- Engage in Sprints: Work at a fast pace in two-week sprints and participate in weekly meetups to share progress and technical challenges.
What skills and knowledge should you bring?
- Academic Background: Currently enrolled in a PhD program in Computer Science, Software Engineering, or a related quantitative field, graduating in 2027
- Python Proficiency: Excellent modern Python engineering skills, with the ability to write readable, performant, and testable code.
- AI Fundamentals: A strong background in AI/ML and experience with independent projects using LLMs, foundation models, or retrieval-augmented generation (RAG).
- System Design: Solid understanding of software engineering principles, including APIs, version control, and system architecture.
- Communication: Excellent communication skills and a collaborative approach to solving complex problems.
- Cybersecurity Interest: Curiosity about applying AI to cybersecurity or hands-on experience in the domain.
Why us?
Our global internship program trains the next-generation of cybersecurity talent across a range of specializations, from threat intelligence to information security, engineering and marketing. Interns can learn about the network security industry from leading thinkers, grow their professional networks, and be part of a career-defining experience including:
- 1:1 mentorship
- The opportunity to expand your knowledge and work on challenging projects
- Training and Development opportunities
- Connections to other recent grads, and employees across the company
- Leadership speaker series where you can learn about other areas of the business and ask questions to the senior leadership team and industry experts
- Fun events!
SentinelOne is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
SentinelOne participates in the E-Verify Program for all U.S. based roles.
Our Purpose
At SentinelOne, we are driven by a clear purpose: to give the advantage to those who secure our future. As AI reshapes how organizations build, operate, and innovate, the responsibility to protect them becomes more critical than ever. When you join SentinelOne, your work helps protect global enterprises, critical infrastructure, and the technologies shaping tomorrow. If you are motivated by meaningful challenges and want your impact to be real, measurable, and global, you will find purpose here.
About us
SentinelOne is a company at the intersection of AI and security, pioneering a new operating model for cybersecurity. Our AI-native platform unifies protection across endpoint, cloud, identity, data, and AI systems to deliver autonomous detection and response with clarity and speed. By combining real-time analytics, intelligent automation, and a unified data foundation, we reduce noise, simplify complexity, and empower security teams to focus on what truly matters.
Our teams are builders, problem-solvers, and innovators committed to shaping the future of security. If you are excited to solve hard problems alongside talented, mission-driven people, we invite you to help us build a safer future for humanity.
What Are We Looking For?
We're looking for people who are relentlessly curious and committed to continuous learning. AI is reshaping every function across our business, and we enable every team member, regardless of role or level, to build fluency in AI tools and concepts. Those who thrive here actively seek out new solutions, experiment thoughtfully, and apply what they learn to drive better, faster, smarter outcomes.
As a motivated PhD student with strong backend fundamentals who is excited about building production AI systems. This role is a great fit for an aspiring software engineer who works comfortably in AI-driven problem spaces and wants to apply software engineering rigor to create LLM-backed products and platforms.
This is not a research-only role. While we work closely with research and science teams, this position sits squarely in a product engineering organization. The focus is on designing and building reliable systems that ship real value to customers and internal users. We value curiosity, experimentation, and a commitment to continuous learning.
What will you do?
As an AI Software Engineering Intern, you will own an end-to-end project from idea to functioning prototype, with a clear path to production. You will:
- Develop Backend Services: Design and build services in Python that power AI-driven products and shared capabilities.
- Integrate Systems: Build resilient service integrations across internal systems, handling failure modes and rate limits.
- Build AI Features: Develop and evolve LLM-backed features and agentic workflows, focusing on reliability and real-world behavior.
- Collaborate Cross-Functionally: Work with product managers, researchers, and senior engineers to turn loosely defined AI use cases into concrete, production-ready systems.
- Shape AI Quality: Help build or extend evaluation harnesses, benchmarks, or feedback loops for AI-powered features.
- Engage in Sprints: Work at a fast pace in two-week sprints and participate in weekly meetups to share progress and technical challenges.
What skills and knowledge should you bring?
- Academic Background: Currently enrolled in a PhD program in Computer Science, Software Engineering, or a related quantitative field, graduating in 2027
- Python Proficiency: Excellent modern Python engineering skills, with the ability to write readable, performant, and testable code.
- AI Fundamentals: A strong background in AI/ML and experience with independent projects using LLMs, foundation models, or retrieval-augmented generation (RAG).
- System Design: Solid understanding of software engineering principles, including APIs, version control, and system architecture.
- Communication: Excellent communication skills and a collaborative approach to solving complex problems.
- Cybersecurity Interest: Curiosity about applying AI to cybersecurity or hands-on experience in the domain.
Why us?
Our global internship program trains the next-generation of cybersecurity talent across a range of specializations, from threat intelligence to information security, engineering and marketing. Interns can learn about the network security industry from leading thinkers, grow their professional networks, and be part of a career-defining experience including:
- 1:1 mentorship
- The opportunity to expand your knowledge and work on challenging projects
- Training and Development opportunities
- Connections to other recent grads, and employees across the company
- Leadership speaker series where you can learn about other areas of the business and ask questions to the senior leadership team and industry experts
- Fun events!
SentinelOne is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
SentinelOne participates in the E-Verify Program for all U.S. based roles.
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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.
Machine Learning Engineer jobs are hiring across the US. Find yours.
Find Machine Learning Engineer 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.
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, 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.
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