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Learning Network Jobs in Arizona (NOW HIRING)

Amazon

Learning Area Manager

Phoenix, AZ · On-site

The Fulfillment Center Learning Manager (LM) is an active member of the FC's Leadership Team, driving and supporting site performance and network initiatives. The Learning Manager actively manages a ...

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Learning Network information

What is a Learning Network job?

A Learning Network job typically involves facilitating knowledge-sharing and collaboration among individuals or organizations. Professionals in this role design, implement, and support learning initiatives, often leveraging digital platforms and communities. They help connect people, curate resources, and foster a culture of continuous learning. This may be done within companies, educational institutions, or industry groups. The goal is to enhance skill development and knowledge exchange across participants.

What are the key skills and qualifications needed to thrive in the Learning Network position, and why are they important?

To excel in a Learning Network role, you should possess expertise in instructional design, educational technology, and organizational learning, often supported by a degree in education or a related field. Familiarity with Learning Management Systems (LMS), collaboration platforms, and data analytics tools is highly valuable. Strong communication, networking, and facilitation skills help foster knowledge sharing and collaboration across diverse groups. These competencies are crucial for effectively building, maintaining, and optimizing networks that support ongoing professional development and learning initiatives within organizations.

What are the main responsibilities of someone working in a Learning Network position?

In a Learning Network role, you are primarily responsible for connecting educators or professionals to share best practices, resources, and knowledge within an organization or across institutions. This often involves designing and facilitating virtual or in-person learning events, managing online communities, and curating educational content. You'll collaborate closely with subject matter experts, IT teams, and stakeholders to ensure the network meets the evolving needs of its members. The role can be both strategic and hands-on, offering opportunities to shape learning culture while directly impacting professional growth.

What are the most commonly searched types of Learning Network jobs in Arizona? The most popular types of Learning Network jobs in Arizona are:
What are popular job titles related to Learning Network jobs in Arizona? For Learning Network jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Learning Network jobs in Arizona look for? The top searched job categories for Learning Network jobs in Arizona are:
What cities in Arizona are hiring for Learning Network jobs? Cities in Arizona with the most Learning Network job openings:
Learning Network jobs near you
Infographic showing various Learning Network job openings in Arizona as of July 2026, with employment types broken down into 1% As Needed, 77% Full Time, 20% Part Time, 1% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution.
Network Engineer, AI Infrastructure Repair

Network Engineer, AI Infrastructure Repair

Meta

Mesa, AZ

$193K/yr

Full-time

Posted 6 days ago

Meta rating

7.5

Company rating: 7.5 out of 10

Based on 44 frontline employees who took The Breakroom Quiz

135th of 209 rated software companies

Job description

Meta is building the next generation of AI infrastructure to power large-scale machine learning workloads, and the reliability of that infrastructure depends on reliable, high-performance network engineering. In this role, you will lead the strategy and execution for AI network repair and remediation programs, ensuring that the high-performance fabrics underpinning Meta's AI training and inference clusters remain operational, resilient, and optimized. You will drive cross-functional initiatives spanning network deployment, fault diagnosis, and repair automation across Meta's AI data center environments, shaping the systems and processes that keep AI infrastructure at scale.
Network Engineer, AI Infrastructure Repair Responsibilities:
  • Define and drive the long-term strategy for AI network repair and remediation programs across large-scale data center environments supporting machine learning workloads
  • Lead root cause analysis and resolution of complex network faults affecting high-performance AI training and inference fabrics, including RDMA, high-speed Ethernet, and optical interconnect layers
  • Develop and champion novel approaches to network fault detection, automated remediation, and repair workflow optimization for AI cluster infrastructure
  • Partner with hardware, software, and data center operations teams to align network repair programs with AI infrastructure deployment roadmaps and capacity plans
  • Establish and refine operational frameworks, runbooks, and tooling for network repair at scale, reducing mean time to repair across AI fabric environments
  • Identify systemic reliability risks in AI network infrastructure and drive cross-functional initiatives to address them before they impact production workloads
  • Influence the design of next-generation AI network architectures by contributing repair and reliability insights to hardware and topology decisions
  • Leverage AI-driven analytics and automation tools to redesign repair workflows, accelerating fault identification and resolution across distributed network environments
  • Build and maintain strategic relationships with internal engineering, operations, and vendor partners to ensure repair programs scale with AI infrastructure growth
  • Communicate program status, risk, and strategic recommendations to engineering leaders and cross-functional stakeholders through structured reporting and executive briefings

Minimum Qualifications:
  • Experience influencing technical direction and organizational strategy through data-driven analysis, written proposals, and stakeholder alignment across engineering and operations teams
  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • Experience leading cross-functional programs that span network operations, hardware deployment, and infrastructure reliability at data center scale
  • Experience developing and driving strategy for network fault management, repair automation, or remediation programs in production environments
  • Experience designing, deploying, or operating high-speed network fabrics used in AI or machine learning infrastructure, including technologies such as RDMA over Converged Ethernet, InfiniBand, or high-density optical interconnects
  • 12+ years of experience in network engineering, with a focus on large-scale data center or high-performance computing network environments

Preferred Qualifications:
  • Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
  • Experience with network telemetry platforms, observability tooling, or AI-assisted anomaly detection applied to large-scale fabric environments
  • Experience building or scaling repair operations programs, including workforce planning, tooling development, and process standardization across multiple data center sites
  • Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • Track record of contributing to network hardware or topology design reviews, translating operational repair insights into upstream engineering improvements
  • Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
  • Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
  • Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
  • Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
  • Familiarity with AI accelerator interconnect architectures and the network reliability requirements of distributed training workloads at hyperscale

About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.
$193,000/year to $271,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.

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