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About This Role
This position requires office presence of a minimum of 5 days per week and is only located in the location(s) posted. No relocation is offered.
AT&T is transforming network operations through AI, automation, and software-defined technologies. We are seeking a Sr Specialist Member of Technical Staff (AI & Network Automation Engineer) to help drive the next generation of intelligent operational platforms that power one of the world's largest telecommunications networks.
In this role, you'll help develop, integrate, validate, and automate enterprise platforms and operational systems that support network provisioning, service delivery, observability, orchestration, and autonomous operations. You'll work at the intersection of software engineering, telecommunications, AI, automation, and operational transformation, partnering with network engineering, architecture, operations, and development teams to deliver innovative solutions that improve reliability, scalability, and operational efficiency.
If you're passionate about leveraging AI and software automation to modernize large-scale telecommunications environments, we'd love to hear from you.
What You'll Do:
Build Intelligent Network Automation Solutions
- Design, develop, test, and support AI-driven automation solutions that improve network operations, service delivery, and operational efficiency.
- Develop and enhance automation workflows leveraging APIs, orchestration platforms, enterprise systems, and AI technologies.
- Validate inventory synchronization, service orchestration, workflow execution, telemetry collection, and platform integrations.
Develop & Validate Operational Platforms
- Build, deploy, and maintain N/N+1 lab environments supporting telecommunications platforms, automation systems, and OSS ecosystems.
- Create realistic test environments that replicate production network architectures and operational workflows.
- Support software upgrades, feature validation, interoperability testing, and operational readiness initiatives.
Enable Network & Service Onboarding
- Support onboarding of network infrastructure, applications, and services into automation and operational platforms.
- Validate provisioning, activation, assurance, inventory management, and lifecycle management workflows.
- Ensure connectivity, configuration accuracy, and operational data integrity across systems.
Drive Troubleshooting & Operational Intelligence
- Analyze logs, telemetry, events, and operational datasets to identify issues and improve platform performance.
- Perform root-cause analysis and collaborate across engineering teams to resolve complex integration challenges.
- Contribute to observability, monitoring, and operational intelligence initiatives.
Advance AI & Next-Generation Operations
- Support development of AI, Machine Learning, and Generative AI capabilities that enhance operational decision-making.
- Contribute to intelligent automation initiatives involving: Test Automation Predictive Analytics Log Analysis Event Correlation Anomaly Detection Workflow Optimization
- Evaluate emerging technologies that improve automation, visibility, and network operations.
Create Technical Documentation & Best Practices
- Develop deployment guides, validation plans, onboarding procedures, architecture documentation, troubleshooting runbooks, and operational standards.
- Share knowledge and contribute to engineering excellence across teams.
What You'll Bring:
- Passion for solving complex operational challenges through AI, software engineering, and automation.
- Strong understanding of telecommunications network operations and modern OSS ecosystems.
- Experience collaborating across software engineering, network architecture, operations, and platform teams.
- Ability to translate business and operational requirements into scalable automation solutions.
- Strong analytical, troubleshooting, documentation, and communication skills.
- Curiosity to evaluate emerging AI and automation technologies that improve operational outcomes.
- A mindset focused on innovation, continuous improvement, and operational excellence.
What You'll Need:
Telecommunications Expertise
- Experience working with telecommunications network environments, including IP, transport, optical, wireless, or service delivery architectures.
- Knowledge of OSS platforms, network lifecycle management, service provisioning, activation, inventory, assurance, and operational workflows.
- Understanding of network automation, orchestration, controller-based architectures, and operational support systems.
- Familiarity with network observability, telemetry, monitoring, and performance management solutions.
Software Engineering & Automation
- Strong programming skills in Python or similar languages used for automation, testing, and analytics.
- Experience with APIs, microservices, distributed systems, and enterprise platform integrations.
- Hands-on experience with Linux/Unix environments, Git, containers, CI/CD pipelines, and automation frameworks.
- Experience developing, testing, and maintaining scalable software-based solutions.
AI & Data Technologies
- Knowledge of AI, Machine Learning, Generative AI, intelligent automation, or advanced analytics solutions.
- Experience applying AI to operational use cases such as automation, anomaly detection, predictive analytics, or workflow optimization.
Education
- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Telecommunications Engineering, Data Science, or a related technical discipline.
Equivalent combination of education and relevant experience may be *
Preferred Qualifications
- Experience supporting telecommunications service providers, network operators, or large-scale enterprise network environments.
- Experience with OSS modernization, network transformation, cloud-native platforms, or autonomous network initiatives.
- Exposure to software-defined networking (SDN), network orchestration, observability platforms, and AI-powered operations.
- Ability to bridge telecommunications domain expertise with software engineering and AI-driven automation.
Our Sr Specialist Member of Technical Staff jobs earn between $128,100.00 - $192,100.00 USD Annual. Not to mention all the other amazing rewards that working at AT&T offers. Individual starting salary within this range may depend on geography, experience, expertise, and education/training. Joining our team comes with amazing perks and benefits:
- Medical/Dental/Vision coverage
- 401(k) plan
- Tuition reimbursement program
- Paid Time Off and Holidays (based on date of hire, at least 23 days of vacation each year and 9 company-designated holidays)
- Paid Parental Leave
- Paid Caregiver Leave
- Additional sick leave beyond what state and local law require may be available but is unprotected
- Adoption Reimbursement
- Disability Benefits (short term and long term)
- Life and Accidental Death Insurance
- Supplemental benefit programs: critical illness/accident hospital indemnity/group legal
- Employee Assistance Programs (EAP)
- Extensive employee wellness programs
- Employee discounts up to 50% off on eligible AT&T mobility plans and accessories, AT&T internet (and fiber where available) and AT&T phone
Weekly Hours:
40Time Type:
RegularLocation:
Dallas, Texas, Plano, Texas, USA:NJ:Middletown / S Laurel Ave - Bldg A:200 S Laurel Ave Bldg ASalary Range:
$128,100.00 - $192,100.00
AT&T and its subsidiaries are committed to equal employment opportunity. All hiring, promotion, and other employment decisions remain merit-based and free from discrimination on the basis of race, color, religion, religious creed, national origin, ancestry, age, sex, sexual orientation, gender, gender identity, gender expression, physical disability, mental disability, pregnancy, medical condition, genetic information, marital status, citizenship status, military status, veteran status, or any other characteristic protected by federal, state, or local laws. In addition, AT&T will provide reasonable accommodations to qualified individuals with disabilities. AT&T is a fair chance employer and does not initiate a background check until an offer is made. Click here to learn more or request an application accommodation here.
Salary Context
This $128K-$192K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At AT&T, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($160K) sits 24% below the category median. Disclosed range: $128K to $192K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
AT&T AI Hiring
AT&T has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Middletown, NJ, US, Atlanta, GA. Compensation range: $192K - $192K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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