Machine Learning Visa Sponsorship Jobs in Arizona
Arizona's machine learning job market is anchored by Intel's semiconductor research campus in Chandler, Honeywell's aerospace AI division in Phoenix, and a growing cluster of fintech and health-tech firms in Scottsdale. Arizona State University's applied AI programs feed a strong local talent pipeline, and many employers in the state actively sponsor H-1B visas for qualified machine learning engineers and researchers.
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Are you ready to take the next step in your career? Join us for an exciting opportunity at Albertsons Companies, where innovation and customer service go hand-in-hand!
At Albertsons Companies, we are looking for someone who’s not just seeking a job, but someone who wants to make an impact. In this role, you’ll have the opportunity to lead, innovate, and contribute to the growth of a company that values great service and lasting customer relationships. This position offers the chance to work in a fast-paced, dynamic environment that’s constantly evolving.
This role is an individual contributor position responsible for designing, developing, fine-tuning, and operationalizing AI/ML capabilities for the AIOps platform within the Observability product. The candidate will work closely with the Lead Engineer, SRE teams, Platform team, Data Ingestion team, Platform DevOps team, Visualization team, and other portfolio teams.
As part of the AIOps Platform team, you will design and build intelligent systems that improve observability, incident response, and operational efficiency. This includes developing machine learning models for forecasting, anomaly prediction, alert classification, event intelligence, and causal analysis, as well as building AI agents and multi-agent workflows for RCA summarization, investigative assistance, and SRE productivity use cases.
This position will be based out of Phoenix, Arizona or Pleasanton CA.
Main responsibilities:
- Design, develop, and productionize AI/ML capabilities for the AIOps platform to support intelligent observability and operational decision-making.
- Build machine learning systems for time-series forecasting, anomaly prediction, incident prediction, alert classification, noise reduction, and event correlation.
- Develop causal ML and statistical inference solutions to identify likely root causes, dependency impacts, and relationships across systems and services.
- Create and fine-tune models for incident intelligence use cases such as forecasting service degradation, capacity risk prediction, alert prioritization, and anomaly explanation.
- Design feature pipelines and model training workflows using telemetry, log, metric, trace, topology, and incident data.
- Build intelligent RCA summarization capabilities using LLMs and agentic frameworks such as LangChain and LangGraph.
- Develop AI agents and multi-agent systems for use cases such as Multi-Agent RCA, SRE Assistant, remediation guidance, incident triage, and operational knowledge retrieval.
- Design prompt orchestration, reasoning workflows, retrieval pipelines, tool usage patterns, and memory/context handling for AI agents.
- Integrate AI/ML services with observability platforms, event systems, knowledge bases, CMDB, incident management tools, and automation platforms.
- Collaborate with platform and engineering teams to build scalable model-serving and agent-serving architectures.
- Define and implement evaluation frameworks for model quality, agent effectiveness, hallucination reduction, relevance, and operational usefulness.
- Ensure AI/ML systems are scalable, reliable, explainable, and aligned with enterprise security, governance, and responsible AI practices.
- Build and maintain APIs and microservices for model inference, online scoring, batch predictions, and agent orchestration.
- Partner with SREs, observability engineers, and product stakeholders to translate operational pain points into ML and AI-driven solutions.
- Continuously improve model performance, feature quality, inference latency, agent reliability, and business impact through experimentation and monitoring.
- Establish engineering best practices for ML development, prompt engineering, evaluation, model deployment, testing, versioning, and documentation.
- Support production incident analysis for AI/ML services and drive root cause identification and remediation for model or agent failures.
- Create technical documentation covering model design, feature logic, training pipelines, evaluation metrics, deployment architecture, and agent workflows.
Drive innovation in AI-enabled observability, causal intelligence, and agentic SRE workflows to enhance the value of the AIOps platform.
We are searching for someone with the following skills:
- Strong experience designing and building AI/ML systems for real-world production use cases.
- Solid hands-on experience with Python and common ML frameworks and libraries such as scikit-learn, XGBoost, PyTorch, TensorFlow, Pandas, and NumPy.
- Experience building machine learning solutions for forecasting, anomaly detection, prediction, classification, clustering, ranking, or recommendation problems.
- Strong understanding of time-series modeling techniques for forecasting and operational prediction use cases.
- Experience with alert classification, incident prediction, event deduplication, prioritization, or signal correlation in observability or IT operations contexts.
- Knowledge of causal ML, causal inference, graph-based reasoning, and dependency-aware analysis techniques for RCA and impact analysis.
- Hands-on experience building AI applications using LLM frameworks such as LangChain and LangGraph.
- Experience designing AI agents or multi-agent systems for reasoning, summarization, task orchestration, troubleshooting, or assistant workflows.
- Strong understanding of prompt engineering, RAG architecture, embeddings, vector stores, tool calling, memory handling, and agent evaluation techniques.
- Experience integrating LLM systems with enterprise tools, APIs, knowledge repositories, and operational systems.
- Experience building backend services and APIs for AI/ML model inference and agent orchestration.
- Good understanding of observability data such as logs, metrics, traces, topology, incidents, and alerts.
- Experience with data engineering concepts including feature engineering, data preprocessing, model pipelines, and batch or streaming inference.
- Familiarity with graph databases such as Neo4j and their use in dependency mapping, causal analysis, and knowledge-driven AI systems.
- Experience with REST APIs, microservices architecture, Docker, Kubernetes, and cloud-native deployment patterns.
- Familiarity with CI/CD, MLOps, model lifecycle management, experiment tracking, and model versioning practices.
- Knowledge of OpenTelemetry, monitoring systems, and observability platforms is highly desirable.
- Strong understanding of software engineering fundamentals, system design, and scalable architecture patterns.
- Strong analytical and problem-solving skills, with the ability to convert ambiguous operational problems into measurable AI/ML solutions.
- Excellent communication and collaboration skills to work with SREs, platform engineers, product owners, and business stakeholders.
- Self-driven mindset with strong curiosity, innovation, and the ability to learn and apply emerging AI techniques effectively.
We believe the successful candidate has these qualifications and experience:
- Bachelor’s degree in computer science, Information Systems, Engineering, Data Science, Artificial Intelligence, or a related field, or equivalent practical experience.
- 6 to 10 plus years of overall experience in software engineering, machine learning, or AI system development.
- 3 plus years of hands-on experience building and deploying machine learning systems in production.
- Strong experience in Python-based AI/ML development is required.
- Experience working on observability, monitoring, or AIOps-related platforms is strongly preferred.
- Experience building LLM-powered applications, AI agents, or multi-agent workflows for enterprise use cases is highly preferred.
- Experience in AIOps, Observability, SRE, IT operations, or incident management domains.
- Experience applying AI/ML to RCA, anomaly explanation, incident summarization, service health prediction, or remediation recommendations.
- Familiarity with knowledge graphs and graph-based ML techniques for dependency-aware intelligence.
- Experience using vector databases and retrieval frameworks for enterprise search and agentic applications.
- Experience integrating AI services with tools such as ServiceNow, Grafana, Prometheus, Splunk, AppDynamics, or similar platforms.
Familiarity with MCP-based client or agent integrations is a plus.
We also provide a variety of benefits including:
- Competitive wages paid weekly
- Access to up to 50% of your earned wages before payday, via our partnership with Stream
- Associate discounts
- Health and financial well-being benefits for eligible associates (Medical, Dental, 401k and more!)
- Time off (vacation, holidays, sick pay). For eligibility requirements please visit myACI Benefits
- Leaders invested in your training, career growth and development
- An inclusive work environment with talented colleagues who reflect the communities we serve
Pay Transparency:
Starting rates will be no less than the local minimum wage and may vary based on criteria such as location, experience, and qualifications. Candidates with unique qualifications may be considered for compensation above this range. Benefits may include medical, dental, vision, disability and life insurance, sick pay, PTO/Vacation Pay or Flexible Time Off, paid holidays, bereavement pay, and retirement benefits (pension and/or 401k eligibility). Associates in this position may be eligible for a quarterly bonus.
Albertsons Companies is at the forefront of the revolution in retail. Committed to innovation and fostering a culture of belonging, our team is united with a unique purpose: to bring people together around the joys of food and to inspire well-being. We want talented individuals to be part of this journey!
Locally great and nationally strong, Albertsons Companies (NYSE: ACI) is a leading food and drug retailer in the U.S. We operate over 2,200 stores, 1,732 pharmacies, 405 fuel centers, 22 distribution facilities, and 19 manufacturing plants across 34 states and the District of Columbia. Our well-known banners include Albertsons, Safeway, Vons, Jewel-Osco, ACME, Shaw’s, Tom Thumb, United Supermarkets, United Express, Randalls, Albertson’s Market, Pavilions, Star Markets, Market Street, Carrs, Haggen, Lucky, Amigos, Andronico’s Community Markets, King’s, Balducci’s, and Albertson’s Market Street.
Our vision is to be a retail leader admired for national strength with deep local roots, offering an easy, fun, friendly, and inspiring experience, no matter how customers choose to shop with us. We celebrate the rich diversity of the communities we serve, and strive to create a workplace where everyone has equal access to opportunities and resources, and can fully contribute to their and our company’s success.
Bring your flavor
Building the future of food and well-being starts with you. Join our team and bring your best self to the table.
Disclaimer
The above statements are intended to describe the general nature of work performed by the employees assigned to this job and are not the official job description for the position. All employees must comply with Company, Division, and Store policies and applicable laws. The responsibilities, duties, and skills of personnel may vary within store and/or from store to store and the official job description will be provided during the application process.
Albertsons is an Equal Opportunity Employer
This Company is an Equal Opportunity Employer, and does not discriminate on the basis of race, gender, ethnicity, religion, national origin, age, disability, veteran status, gender identity/expression, sexual orientation, or on any other basis prohibited by law. Consistent with applicable state and local law, the Company will consider for employment qualified applicants with arrest and conviction records.
We endeavor to make this site accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at 1-888-255-2269(option #4).
Why choose us?
Are you ready to take the next step in your career? Join us for an exciting opportunity at Albertsons Companies, where innovation and customer service go hand-in-hand!
At Albertsons Companies, we are looking for someone who’s not just seeking a job, but someone who wants to make an impact. In this role, you’ll have the opportunity to lead, innovate, and contribute to the growth of a company that values great service and lasting customer relationships. This position offers the chance to work in a fast-paced, dynamic environment that’s constantly evolving.
This role is an individual contributor position responsible for designing, developing, fine-tuning, and operationalizing AI/ML capabilities for the AIOps platform within the Observability product. The candidate will work closely with the Lead Engineer, SRE teams, Platform team, Data Ingestion team, Platform DevOps team, Visualization team, and other portfolio teams.
As part of the AIOps Platform team, you will design and build intelligent systems that improve observability, incident response, and operational efficiency. This includes developing machine learning models for forecasting, anomaly prediction, alert classification, event intelligence, and causal analysis, as well as building AI agents and multi-agent workflows for RCA summarization, investigative assistance, and SRE productivity use cases.
This position will be based out of Phoenix, Arizona or Pleasanton CA.
Main responsibilities:
- Design, develop, and productionize AI/ML capabilities for the AIOps platform to support intelligent observability and operational decision-making.
- Build machine learning systems for time-series forecasting, anomaly prediction, incident prediction, alert classification, noise reduction, and event correlation.
- Develop causal ML and statistical inference solutions to identify likely root causes, dependency impacts, and relationships across systems and services.
- Create and fine-tune models for incident intelligence use cases such as forecasting service degradation, capacity risk prediction, alert prioritization, and anomaly explanation.
- Design feature pipelines and model training workflows using telemetry, log, metric, trace, topology, and incident data.
- Build intelligent RCA summarization capabilities using LLMs and agentic frameworks such as LangChain and LangGraph.
- Develop AI agents and multi-agent systems for use cases such as Multi-Agent RCA, SRE Assistant, remediation guidance, incident triage, and operational knowledge retrieval.
- Design prompt orchestration, reasoning workflows, retrieval pipelines, tool usage patterns, and memory/context handling for AI agents.
- Integrate AI/ML services with observability platforms, event systems, knowledge bases, CMDB, incident management tools, and automation platforms.
- Collaborate with platform and engineering teams to build scalable model-serving and agent-serving architectures.
- Define and implement evaluation frameworks for model quality, agent effectiveness, hallucination reduction, relevance, and operational usefulness.
- Ensure AI/ML systems are scalable, reliable, explainable, and aligned with enterprise security, governance, and responsible AI practices.
- Build and maintain APIs and microservices for model inference, online scoring, batch predictions, and agent orchestration.
- Partner with SREs, observability engineers, and product stakeholders to translate operational pain points into ML and AI-driven solutions.
- Continuously improve model performance, feature quality, inference latency, agent reliability, and business impact through experimentation and monitoring.
- Establish engineering best practices for ML development, prompt engineering, evaluation, model deployment, testing, versioning, and documentation.
- Support production incident analysis for AI/ML services and drive root cause identification and remediation for model or agent failures.
- Create technical documentation covering model design, feature logic, training pipelines, evaluation metrics, deployment architecture, and agent workflows.
Drive innovation in AI-enabled observability, causal intelligence, and agentic SRE workflows to enhance the value of the AIOps platform.
We are searching for someone with the following skills:
- Strong experience designing and building AI/ML systems for real-world production use cases.
- Solid hands-on experience with Python and common ML frameworks and libraries such as scikit-learn, XGBoost, PyTorch, TensorFlow, Pandas, and NumPy.
- Experience building machine learning solutions for forecasting, anomaly detection, prediction, classification, clustering, ranking, or recommendation problems.
- Strong understanding of time-series modeling techniques for forecasting and operational prediction use cases.
- Experience with alert classification, incident prediction, event deduplication, prioritization, or signal correlation in observability or IT operations contexts.
- Knowledge of causal ML, causal inference, graph-based reasoning, and dependency-aware analysis techniques for RCA and impact analysis.
- Hands-on experience building AI applications using LLM frameworks such as LangChain and LangGraph.
- Experience designing AI agents or multi-agent systems for reasoning, summarization, task orchestration, troubleshooting, or assistant workflows.
- Strong understanding of prompt engineering, RAG architecture, embeddings, vector stores, tool calling, memory handling, and agent evaluation techniques.
- Experience integrating LLM systems with enterprise tools, APIs, knowledge repositories, and operational systems.
- Experience building backend services and APIs for AI/ML model inference and agent orchestration.
- Good understanding of observability data such as logs, metrics, traces, topology, incidents, and alerts.
- Experience with data engineering concepts including feature engineering, data preprocessing, model pipelines, and batch or streaming inference.
- Familiarity with graph databases such as Neo4j and their use in dependency mapping, causal analysis, and knowledge-driven AI systems.
- Experience with REST APIs, microservices architecture, Docker, Kubernetes, and cloud-native deployment patterns.
- Familiarity with CI/CD, MLOps, model lifecycle management, experiment tracking, and model versioning practices.
- Knowledge of OpenTelemetry, monitoring systems, and observability platforms is highly desirable.
- Strong understanding of software engineering fundamentals, system design, and scalable architecture patterns.
- Strong analytical and problem-solving skills, with the ability to convert ambiguous operational problems into measurable AI/ML solutions.
- Excellent communication and collaboration skills to work with SREs, platform engineers, product owners, and business stakeholders.
- Self-driven mindset with strong curiosity, innovation, and the ability to learn and apply emerging AI techniques effectively.
We believe the successful candidate has these qualifications and experience:
- Bachelor’s degree in computer science, Information Systems, Engineering, Data Science, Artificial Intelligence, or a related field, or equivalent practical experience.
- 6 to 10 plus years of overall experience in software engineering, machine learning, or AI system development.
- 3 plus years of hands-on experience building and deploying machine learning systems in production.
- Strong experience in Python-based AI/ML development is required.
- Experience working on observability, monitoring, or AIOps-related platforms is strongly preferred.
- Experience building LLM-powered applications, AI agents, or multi-agent workflows for enterprise use cases is highly preferred.
- Experience in AIOps, Observability, SRE, IT operations, or incident management domains.
- Experience applying AI/ML to RCA, anomaly explanation, incident summarization, service health prediction, or remediation recommendations.
- Familiarity with knowledge graphs and graph-based ML techniques for dependency-aware intelligence.
- Experience using vector databases and retrieval frameworks for enterprise search and agentic applications.
- Experience integrating AI services with tools such as ServiceNow, Grafana, Prometheus, Splunk, AppDynamics, or similar platforms.
Familiarity with MCP-based client or agent integrations is a plus.
We also provide a variety of benefits including:
- Competitive wages paid weekly
- Access to up to 50% of your earned wages before payday, via our partnership with Stream
- Associate discounts
- Health and financial well-being benefits for eligible associates (Medical, Dental, 401k and more!)
- Time off (vacation, holidays, sick pay). For eligibility requirements please visit myACI Benefits
- Leaders invested in your training, career growth and development
- An inclusive work environment with talented colleagues who reflect the communities we serve
Pay Transparency:
Starting rates will be no less than the local minimum wage and may vary based on criteria such as location, experience, and qualifications. Candidates with unique qualifications may be considered for compensation above this range. Benefits may include medical, dental, vision, disability and life insurance, sick pay, PTO/Vacation Pay or Flexible Time Off, paid holidays, bereavement pay, and retirement benefits (pension and/or 401k eligibility). Associates in this position may be eligible for a quarterly bonus.
Albertsons Companies is at the forefront of the revolution in retail. Committed to innovation and fostering a culture of belonging, our team is united with a unique purpose: to bring people together around the joys of food and to inspire well-being. We want talented individuals to be part of this journey!
Locally great and nationally strong, Albertsons Companies (NYSE: ACI) is a leading food and drug retailer in the U.S. We operate over 2,200 stores, 1,732 pharmacies, 405 fuel centers, 22 distribution facilities, and 19 manufacturing plants across 34 states and the District of Columbia. Our well-known banners include Albertsons, Safeway, Vons, Jewel-Osco, ACME, Shaw’s, Tom Thumb, United Supermarkets, United Express, Randalls, Albertson’s Market, Pavilions, Star Markets, Market Street, Carrs, Haggen, Lucky, Amigos, Andronico’s Community Markets, King’s, Balducci’s, and Albertson’s Market Street.
Our vision is to be a retail leader admired for national strength with deep local roots, offering an easy, fun, friendly, and inspiring experience, no matter how customers choose to shop with us. We celebrate the rich diversity of the communities we serve, and strive to create a workplace where everyone has equal access to opportunities and resources, and can fully contribute to their and our company’s success.
Bring your flavor
Building the future of food and well-being starts with you. Join our team and bring your best self to the table.
Disclaimer
The above statements are intended to describe the general nature of work performed by the employees assigned to this job and are not the official job description for the position. All employees must comply with Company, Division, and Store policies and applicable laws. The responsibilities, duties, and skills of personnel may vary within store and/or from store to store and the official job description will be provided during the application process.
Albertsons is an Equal Opportunity Employer
This Company is an Equal Opportunity Employer, and does not discriminate on the basis of race, gender, ethnicity, religion, national origin, age, disability, veteran status, gender identity/expression, sexual orientation, or on any other basis prohibited by law. Consistent with applicable state and local law, the Company will consider for employment qualified applicants with arrest and conviction records.
We endeavor to make this site accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at 1-888-255-2269(option #4).
Machine Learning Job Roles in Arizona
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Search Machine Learning Jobs in ArizonaMachine Learning Jobs in Arizona: Frequently Asked Questions
Which companies sponsor visas for machine learning roles in Arizona?
Intel, Honeywell, and Microchip Technology are among the most consistent H-1B sponsors for machine learning roles in Arizona, particularly in the Phoenix metro area. Health-tech companies like Banner Health and Dignity Health have also filed sponsorships for AI and ML positions. Scottsdale-based fintech firms and several semiconductor startups in the Chandler and Tempe corridors round out the sponsoring employer base.
Which visa types are most common for machine learning roles in Arizona?
The H-1B is the most common visa category for machine learning engineers and researchers in Arizona, as ML roles consistently meet the specialty occupation standard requiring a bachelor's degree or higher in computer science, data science, or a related field. Candidates with extraordinary ability may qualify for the O-1A. Graduates from Arizona universities often begin on OPT or STEM OPT before transitioning to H-1B sponsorship.
Which cities in Arizona have the most machine learning sponsorship jobs?
Phoenix and its immediate suburbs account for the majority of machine learning visa sponsorship activity in Arizona. Chandler is a significant hub due to Intel's large semiconductor campus. Tempe benefits from its proximity to Arizona State University and attracts mid-stage tech companies. Scottsdale draws fintech and health-tech employers with ML teams. Tucson has a smaller but active market tied to University of Arizona research partnerships.
How to find machine learning visa sponsorship jobs in Arizona?
Migrate Mate lets you filter machine learning jobs specifically by visa sponsorship availability and state, making it straightforward to identify Arizona employers actively hiring international candidates. Because sponsorship willingness varies widely even within the same industry, filtering to confirmed sponsors saves significant time. Migrate Mate's listings cover roles across Phoenix, Chandler, Tempe, and Scottsdale, where Arizona's ML hiring is most concentrated.
Are there any Arizona-specific considerations for machine learning job seekers seeking sponsorship?
Arizona's growing semiconductor and aerospace sectors create ML demand that differs from pure software markets, meaning candidates with hardware-adjacent ML skills, such as edge inference or sensor data modeling, may find stronger fit here than in coastal tech hubs. Arizona State University's partnerships with Intel and local industry also produce a well-connected alumni network. Prevailing wage requirements for H-1B petitions still apply and are set by the Department of Labor based on the specific role and location.
What is the prevailing wage for sponsored machine learning jobs in Arizona?
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.