ML Software Engineer Visa Sponsorship Jobs in California
California leads the country for ML software engineer visa sponsorship jobs, with major employers like Google, Meta, Apple, and dozens of AI-focused startups concentrated in the Bay Area, Los Angeles, and San Diego. The state's deep university pipeline from UC Berkeley, Stanford, and UCLA feeds consistent demand for international ML talent across research and production engineering roles.
Find ML Software Engineer JobsOverview
Showing 5 of 1,757+ ML Software Engineer Jobs in California with Visa Sponsorship
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
See all 1,757+ ML Software Engineer Jobs in California with Visa Sponsorship
Sign up for free to unlock all listings, filter by visa type, and get alerts for new ML Software Engineer Jobs in California with Visa Sponsorship.
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.
ML Software Engineer Job Roles in California
See all 1,757+ ML Software Engineer Jobs in California
Sign up for free to filter by visa type, set job alerts, and find employers with verified sponsorship history.
Search ML Software Engineer Jobs in CaliforniaML Software Engineer Jobs in California: Frequently Asked Questions
Which companies sponsor visas for ML software engineers in California?
Large technology companies headquartered in California, including Google, Apple, Meta, and NVIDIA, are among the most active sponsors for ML software engineer roles. Beyond the major players, a significant number of AI-focused startups and mid-sized companies in the Bay Area and Los Angeles regularly file H-1B visa petitions for ML engineers. DOL disclosure data consistently shows California employers among the highest-volume H-1B filers for machine learning and AI-related job titles.
Which visa types are most common for ML software engineer roles in California?
The H-1B is the most common visa for ML software engineers in California, as the role typically qualifies as a specialty occupation requiring a bachelor's degree or higher in computer science, statistics, or a closely related field. Candidates with extraordinary recognition in ML research may pursue the O-1A. Those completing degrees at California universities may first work under OPT or STEM OPT, which provides up to three years of work authorization before transitioning to employer-sponsored status.
Which cities in California have the most ML software engineer sponsorship jobs?
The San Francisco Bay Area, encompassing San Francisco, San Jose, Mountain View, Palo Alto, and Sunnyvale, concentrates the largest share of ML software engineer sponsorship jobs in California. Los Angeles has grown significantly as a second hub, driven by entertainment technology, autonomous vehicle research, and a maturing startup ecosystem. San Diego also has a presence, particularly tied to biotech and defense-adjacent ML applications.
How to find ml software engineer visa sponsorship jobs in California?
Migrate Mate is built specifically for international job seekers and filters ML software engineer roles in California by visa sponsorship availability, saving you from sorting through positions at companies unlikely to sponsor. Because California's ML hiring spans both large tech employers and early-stage startups, Migrate Mate helps you identify which companies have an active sponsorship track record rather than relying on job descriptions that rarely confirm sponsorship status upfront.
Are there any California-specific considerations for ML software engineers seeking visa sponsorship?
California's high prevailing wage determinations, set by the Department of Labor, reflect the state's elevated cost of living and competitive compensation norms, meaning employers must meet higher certified wage thresholds for ML engineering roles here than in most other states. California also has some of the country's strongest employee protection laws, which can affect how employment contracts and non-compete clauses are structured. The state's dense university pipeline from institutions like UC Berkeley, Stanford, and UCLA means international students on STEM OPT often transition directly into sponsored roles with California employers.
What is the prevailing wage for sponsored ml software engineer jobs in California?
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.