Green Card ML Software Engineer Jobs
ML Software Engineer roles at U.S. companies regularly qualify for EB-2 and EB-3 green card sponsorship through the PERM labor certification process. Employers document that no qualified U.S. worker is available before filing your I-140 petition, making your specialized skills in machine learning the foundation of your sponsorship case.
Find Green Card ML Software Engineer JobsOverview
Showing 5 of 3,611+ ML Software Engineer jobs
See all 3,611+ ML Software Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new ML Software Engineer roles.
Get Access To All JobsINTRODUCTION
The Senior Principal AI Agent / ML Software Engineer is a Senior Staff-level, hands-on technical leadership role responsible for defining, building, and operating next-generation AI systems on Oracle Cloud Infrastructure (OCI). This person will set architecture and engineering direction for production-grade agentic AI platforms, autonomous workflows, scalable inference infrastructure, and enterprise AI applications used in large-scale, business-critical environments.
This role requires a proven engineer who can translate ambiguous product and platform goals into durable technical strategy, lead multi-team execution without direct authority, and remain deeply hands-on in design, code, reviews, operations, and incident follow-up. The ideal candidate combines deep distributed systems experience with practical AI-native engineering, including orchestration of LLMs, tools, APIs, memory, retrieval, evaluation, guardrails, and cloud services. The expectation is to ship, scale, and operate reliable, secure, observable, and cost-aware AI platform systems while raising the technical bar for engineers across the organization.
Responsibilities
- Serve as a senior technical owner for OCI AI platform capabilities, including agent execution, inference systems, model serving, AI workflow orchestration, evaluation, and observability.
- Design, architect, and deliver scalable agentic AI systems capable of reasoning, planning, tool use, workflow execution, multi-step task orchestration, and safe human-in-the-loop escalation.
- Build production-grade services for tool calling, agent memory, context management, Model Context Protocol (MCP) integration, vector retrieval, multi-agent coordination, policy enforcement, and evaluation.
- Lead architecture across distributed services optimized for low latency, high throughput, GPU efficiency, reliability, cost, operability, and secure multi-tenant operation.
- Define service boundaries, APIs, data models, state management, consistency tradeoffs, failure modes, SLIs/SLOs, rollout strategies, and operational readiness criteria for AI platform services.
- Drive technical strategy across infrastructure, platform, security, data, and application engineering teams, converting broad goals into executable multi-quarter plans and measurable milestones.
- Integrate AI agents securely and reliably with enterprise APIs, cloud services, databases, identity systems, secrets management, and external systems.
- Establish AgentOps and LLMOps practices for tracing, monitoring, eval suites, regression testing, experimentation, safety guardrails, prompt/tool versioning, and production reliability.
- Evaluate and operationalize emerging technologies in generative AI, agentic workflows, inference optimization, long-context systems, reasoning models, AI developer tooling, and agentic-first development.
- Drive engineering excellence through code reviews, design reviews, test strategy, deployment automation, incident analysis, documentation, and AI-assisted development practices using tools such as Codex, Claude Code, Cursor, Copilot, or similar systems.
- Mentor Staff and senior engineers, raise architectural standards, and influence engineering practices across OCI without requiring direct management authority.
- Own critical production outcomes, including reliability, performance, security posture, cost efficiency, and supportability for the systems delivered.
REQUIRED QUALIFICATIONS
- Bachelor's, Master's, or Ph.D. in Computer Science, AI/ML, Engineering, or a related field, or equivalent practical experience.
- 12+ years of professional software engineering experience, including significant ownership of production systems; or equivalent experience demonstrating Senior Staff / Principal-level impact.
- Proven track record as a Staff, Senior Staff, Principal, or equivalent technical leader influencing architecture and execution across multiple teams.
- Deep experience designing, building, and operating high-scale distributed systems, cloud services, infrastructure platforms, or AI/ML platform services.
- Hands-on experience with production AI systems, agentic AI applications, autonomous workflows, tool-using agents, multi-step orchestration, or multi-agent systems.
- Practical experience with orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen, LlamaIndex, or similar ecosystems.
- Deep understanding of LLM application patterns, including prompt design, structured outputs, function/tool calling, context management, RAG, memory, tool safety, and evaluation.
- Strong programming skills in Python and ability to contribute high-quality production code, reviews, tests, and debugging in complex distributed environments.
- Strong expertise with Kubernetes, Docker, cloud-native infrastructure, service-to-service communication, scalability, fault tolerance, observability, and performance analysis.
- Experience defining SLIs/SLOs, production readiness criteria, incident response practices, monitoring, tracing, experiments, and reliability programs for AI or distributed systems.
- Strong understanding of AI safety, governance, security, and operational risks for autonomous or semi-autonomous systems, including data handling, access control, auditability, and human accountability.
- Excellent written and verbal communication, with demonstrated ability to lead technical direction, resolve ambiguity, and influence senior stakeholders.
PREFERRED QUALIFICATIONS
- Experience optimizing large-scale GPU inference or training workloads for latency, throughput, utilization, availability, and cost.
- Experience building or operating model serving, inference gateways, agent runtimes, workflow engines, developer platforms, or internal AI productivity platforms.
- Experience integrating AI systems with enterprise APIs, databases, cloud services, vector databases, embeddings, retrieval systems, identity systems, and policy enforcement layers.
- Experience with LLM fine-tuning, long-context systems, reasoning models, model routing, caching, batching, quantization, or emerging generative AI research.
- Experience building evaluation frameworks for agentic systems, including offline evals, online experiments, golden tasks, adversarial testing, regression gates, and observability dashboards.
- Experience using AI-assisted software development tools such as Codex, Claude Code, Cursor, Copilot, or similar systems in large-scale engineering environments.
- Track record of defining architectural standards, platform capabilities, or engineering practices adopted across multiple teams or organizations.
- Experience in enterprise, cloud infrastructure, regulated, security-sensitive, or mission-critical environments.
INTRODUCTION
The Senior Principal AI Agent / ML Software Engineer is a Senior Staff-level, hands-on technical leadership role responsible for defining, building, and operating next-generation AI systems on Oracle Cloud Infrastructure (OCI). This person will set architecture and engineering direction for production-grade agentic AI platforms, autonomous workflows, scalable inference infrastructure, and enterprise AI applications used in large-scale, business-critical environments.
This role requires a proven engineer who can translate ambiguous product and platform goals into durable technical strategy, lead multi-team execution without direct authority, and remain deeply hands-on in design, code, reviews, operations, and incident follow-up. The ideal candidate combines deep distributed systems experience with practical AI-native engineering, including orchestration of LLMs, tools, APIs, memory, retrieval, evaluation, guardrails, and cloud services. The expectation is to ship, scale, and operate reliable, secure, observable, and cost-aware AI platform systems while raising the technical bar for engineers across the organization.
Responsibilities
- Serve as a senior technical owner for OCI AI platform capabilities, including agent execution, inference systems, model serving, AI workflow orchestration, evaluation, and observability.
- Design, architect, and deliver scalable agentic AI systems capable of reasoning, planning, tool use, workflow execution, multi-step task orchestration, and safe human-in-the-loop escalation.
- Build production-grade services for tool calling, agent memory, context management, Model Context Protocol (MCP) integration, vector retrieval, multi-agent coordination, policy enforcement, and evaluation.
- Lead architecture across distributed services optimized for low latency, high throughput, GPU efficiency, reliability, cost, operability, and secure multi-tenant operation.
- Define service boundaries, APIs, data models, state management, consistency tradeoffs, failure modes, SLIs/SLOs, rollout strategies, and operational readiness criteria for AI platform services.
- Drive technical strategy across infrastructure, platform, security, data, and application engineering teams, converting broad goals into executable multi-quarter plans and measurable milestones.
- Integrate AI agents securely and reliably with enterprise APIs, cloud services, databases, identity systems, secrets management, and external systems.
- Establish AgentOps and LLMOps practices for tracing, monitoring, eval suites, regression testing, experimentation, safety guardrails, prompt/tool versioning, and production reliability.
- Evaluate and operationalize emerging technologies in generative AI, agentic workflows, inference optimization, long-context systems, reasoning models, AI developer tooling, and agentic-first development.
- Drive engineering excellence through code reviews, design reviews, test strategy, deployment automation, incident analysis, documentation, and AI-assisted development practices using tools such as Codex, Claude Code, Cursor, Copilot, or similar systems.
- Mentor Staff and senior engineers, raise architectural standards, and influence engineering practices across OCI without requiring direct management authority.
- Own critical production outcomes, including reliability, performance, security posture, cost efficiency, and supportability for the systems delivered.
REQUIRED QUALIFICATIONS
- Bachelor's, Master's, or Ph.D. in Computer Science, AI/ML, Engineering, or a related field, or equivalent practical experience.
- 12+ years of professional software engineering experience, including significant ownership of production systems; or equivalent experience demonstrating Senior Staff / Principal-level impact.
- Proven track record as a Staff, Senior Staff, Principal, or equivalent technical leader influencing architecture and execution across multiple teams.
- Deep experience designing, building, and operating high-scale distributed systems, cloud services, infrastructure platforms, or AI/ML platform services.
- Hands-on experience with production AI systems, agentic AI applications, autonomous workflows, tool-using agents, multi-step orchestration, or multi-agent systems.
- Practical experience with orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen, LlamaIndex, or similar ecosystems.
- Deep understanding of LLM application patterns, including prompt design, structured outputs, function/tool calling, context management, RAG, memory, tool safety, and evaluation.
- Strong programming skills in Python and ability to contribute high-quality production code, reviews, tests, and debugging in complex distributed environments.
- Strong expertise with Kubernetes, Docker, cloud-native infrastructure, service-to-service communication, scalability, fault tolerance, observability, and performance analysis.
- Experience defining SLIs/SLOs, production readiness criteria, incident response practices, monitoring, tracing, experiments, and reliability programs for AI or distributed systems.
- Strong understanding of AI safety, governance, security, and operational risks for autonomous or semi-autonomous systems, including data handling, access control, auditability, and human accountability.
- Excellent written and verbal communication, with demonstrated ability to lead technical direction, resolve ambiguity, and influence senior stakeholders.
PREFERRED QUALIFICATIONS
- Experience optimizing large-scale GPU inference or training workloads for latency, throughput, utilization, availability, and cost.
- Experience building or operating model serving, inference gateways, agent runtimes, workflow engines, developer platforms, or internal AI productivity platforms.
- Experience integrating AI systems with enterprise APIs, databases, cloud services, vector databases, embeddings, retrieval systems, identity systems, and policy enforcement layers.
- Experience with LLM fine-tuning, long-context systems, reasoning models, model routing, caching, batching, quantization, or emerging generative AI research.
- Experience building evaluation frameworks for agentic systems, including offline evals, online experiments, golden tasks, adversarial testing, regression gates, and observability dashboards.
- Experience using AI-assisted software development tools such as Codex, Claude Code, Cursor, Copilot, or similar systems in large-scale engineering environments.
- Track record of defining architectural standards, platform capabilities, or engineering practices adopted across multiple teams or organizations.
- Experience in enterprise, cloud infrastructure, regulated, security-sensitive, or mission-critical environments.
See all 3,611+ Green Card ML Software Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Green Card ML Software Engineer Jobs.
Get Access To All JobsTips for Finding Green Card Sponsorship as a ML Software Engineer
Align your credentials to EB-2 requirements
PERM-based EB-2 requires an advanced degree or its equivalent in a directly related field. A bachelor's plus five years of progressive ML experience can substitute for a master's, but document each role's technical scope carefully before your employer files.
Target employers with active PERM filing history
Search DOL's OFLC Wage Search data to identify companies that have previously certified PERM applications for ML or software roles. Prior filing history signals an HR and legal team already familiar with the sponsorship workflow, reducing delays.
Negotiate sponsorship terms before accepting an offer
Clarify in writing whether the employer covers attorney fees, I-140 premium processing, and adjustment of status filing costs. Some companies cap reimbursement at the I-140 stage, leaving you to fund I-485 out of pocket.
Use Migrate Mate to find green card sponsoring employers
Filter by ML Software Engineer roles where employers have documented EB-2 or EB-3 sponsorship history. Migrate Mate surfaces this PERM data so you target companies already running the process, not ones you'd have to educate from scratch.
Understand how your country affects priority date timing
EB-2 and EB-3 backlogs vary sharply by birth country. If you were born in India or China, check the monthly USCIS Visa Bulletin before accepting a role, since your wait for an available priority date can extend years beyond the PERM approval.
Request an O*NET occupational classification from your employer
PERM job duties must match the O*NET profile for your role. Vague or overly broad duty lists get audited. Ask your employer's attorney to verify the SOC code and minimum requirements before the DOL application is submitted.
Green Card ML Software Engineer: Frequently Asked Questions
Do ML Software Engineer roles qualify for EB-2 or EB-3 green card sponsorship?
Most ML Software Engineer positions qualify for EB-2 because employers typically require a master's degree or a bachelor's plus substantial progressive experience in machine learning or a closely related field. If the posted role requires only a bachelor's degree, it may be classified under EB-3 instead. The employer's attorney determines the correct category based on the actual minimum job requirements, not the candidate's credentials.
How does PERM green card sponsorship differ from H-1B for an ML engineer?
The PERM process leads to permanent residency rather than a renewable temporary status, and EB-3 approvals for most countries outside India and China face no meaningful backlog. Unlike H-1B visa, there's no annual lottery and no cap on EB-3 filings per employer. The tradeoff is timeline: PERM labor certification alone can take six to eighteen months before you even reach the I-140 stage, compared to H-1B processing measured in weeks or months.
What documentation should I prepare before a sponsoring employer starts the PERM process?
Gather transcripts showing your qualifying degree, employment verification letters that specify job titles and technical duties for each prior ML role, and any publications or patents that support specialty occupation or EB-2 eligibility. Your employer will need this to define the minimum requirements accurately. Gaps or mismatches between your resume and official records are among the most common causes of PERM audit.
How can I find ML Software Engineer jobs where employers already sponsor green cards?
Migrate Mate lets you search specifically for ML Software Engineer roles at employers with documented EB-2 and EB-3 PERM filing history, so you're not guessing which companies will sponsor. This matters because starting the sponsorship conversation with an employer that has never run PERM before adds months of internal approvals before the DOL application is even prepared.
Can I change employers after my I-140 is approved but before I get my green card?
Yes, in most cases. Once your I-140 has been approved for at least 180 days, portability rules allow you to move to a new employer in a same or similar ML or software role without losing your priority date. The new employer doesn't need to restart PERM from scratch. You'll need your new employer to confirm the role is sufficiently similar and that your adjustment of status application remains pending.