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Ai Reliability Engineer Jobs in Dallas, TX (NOW HIRING)

WEX

SRE Architect, AI-Powered Reliability

Dallas, TX

$56.50 - $75/hr

Define and lead WEX's AI-Powered Reliability Engineering strategy, driving adoption of SRE agents across the software lifecycle-from design and development through deployment and operations, to ...

eNett

SRE Architect, AI-Powered Reliability

Dallas, TX

$56.50 - $75/hr

Define and lead WEX's AI-Powered Reliability Engineering strategy, driving adoption of SRE agents across the software lifecycle-from design and development through deployment and operations, to ...

NTT DATA Services

Site Reliability Engineer

Westlake, TX · On-site +1

$54.75 - $72.75/hr

We are currently seeking a Site Reliability Engineer to join our team in Westlake, Texas (US-TX), ... We are one of the world's leading AI and digital infrastructure providers, with unmatched ...

NTT DATA Services

Site Reliability Engineer

Westlake, TX · On-site

$54.75 - $72.75/hr

We are currently seeking a Site Reliability Engineer to join our team in Westlake, Texas (US-TX), ... We are one of the world's leading AI and digital infrastructure providers, with unmatched ...

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All JobsAi Reliability Engineer JobsAi Reliability Engineer Jobs in Dallas, TX

Ai Reliability Engineer information

See Dallas, TX salary details

$60.3K

$116.7K

$139.5K

How much do ai reliability engineer jobs pay per year?

As of Aug 16, 2026, the average yearly pay for ai reliability engineer in Dallas, TX is $116,703.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,400.00 and $127,600.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an AI reliability engineer, and why are they important?

To thrive as an AI Reliability Engineer, you need a solid background in computer science or engineering, expertise in AI/ML concepts, and experience with software testing and reliability methodologies. Familiarity with tools like TensorFlow, PyTorch, CI/CD pipelines, and reliability testing frameworks, along with certifications in cloud platforms (e.g., AWS Certified Machine Learning), is highly valuable. Analytical thinking, problem-solving abilities, and strong collaboration skills set top performers apart in this role. These skills ensure robust, dependable AI systems that meet performance standards and maintain trust in critical applications.

What is the difference between Ai Reliability Engineer vs Data Scientist?

AspectAi Reliability EngineerData Scientist
Required CredentialsBachelor's or master's in CS, engineering, or related; certifications in AI/MLBachelor's or master's in CS, statistics, or related; certifications in data analysis or ML
Work EnvironmentTech companies, AI-focused teams, engineering departmentsResearch labs, tech firms, analytics teams
Employer & Industry UsageAI product development, machine learning systems, reliability testingData analysis, predictive modeling, business insights

While both roles involve AI and ML, Ai Reliability Engineers focus on ensuring AI system robustness and uptime, whereas Data Scientists analyze data to generate insights and models. The roles often collaborate but serve different primary functions within AI projects.

What is an AI reliability engineer?

AI Reliability Engineers are professionals responsible for ensuring that artificial intelligence systems function reliably, safely, and effectively over time. They work on monitoring AI models in production, identifying and mitigating potential failures, and improving the robustness of AI systems. Their tasks often include testing, validation, performance monitoring, and implementing best practices for maintaining AI infrastructure. By focusing on reliability, they help organizations deploy AI solutions that are dependable and trustworthy in real-world environments.

What are some common challenges AI reliability engineers face when ensuring model robustness in production environments?

Ai Reliability Engineers often encounter challenges such as monitoring AI model performance for drift or unexpected behavior, managing data quality issues, and implementing automated alerting systems for anomalies. In production, it's crucial to ensure that AI models operate consistently and remain reliable under varying conditions and data inputs. Collaborating closely with data scientists, software engineers, and DevOps teams is essential to address these challenges and to continuously improve model reliability and uptime.

What are popular job titles related to Ai Reliability Engineer jobs in Dallas, TX?

For Ai Reliability Engineer jobs in Dallas, TX, the most frequently searched job titles are:

What job categories do people searching Ai Reliability Engineer jobs in Dallas, TX look for?

The top searched job categories for Ai Reliability Engineer jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Ai Reliability Engineer jobs?

Cities near Dallas, TX with the most Ai Reliability Engineer job openings:

Ai Reliability Engineer jobs near you
Infographic showing various Ai Reliability Engineer job openings in Dallas, TX as of August 2026, with employment types broken down into 77% Full Time, 8% Temporary, and 15% Contract. Highlights an 85% In-person, and 15% Remote job distribution, with an average salary of $116,703 per year, or $56.1 per hour.

Staff Hardware Reliability Engineer (R5142)

Shield AI

Dallas, TX • On-site

$158K - $237K/yr

Full-time

Re-posted 13 days ago

Job description

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI's technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. 

Job Description:
As a Hardware Reliability Engineer at Shield AI, you will be responsible for ensuring the robustness and long-term performance of our VBAT flight hardware. You'll work closely with design, manufacturing, and supplier chain to implement design-for-reliability best practices and perform reliability verification from concept through production.
 
You will lead environmental and stress testing efforts, including temperature cycling, vibration, HALT, and HASS, conduct failure analysis and materials characterization, and analyze root cause investigations for manufacturing non-conformances and field returns. You'll participate in design reviews and FMEA activities, shape material selection and manufacturing requirements, analyze test and field data using reliability modeling tools, and help develop corrective actions and process improvements that elevate hardware reliability across the program. 
What you'll do:
  • You will be responsible for developing and implementing design-for-reliability best practices, conducting rigorous testing, shaping manufacturing requirements, selecting materials, and analyzing field data to enhance the robustness of VBAT hardware. 
  • Perform stress screening, environmental testing, and drive failure analysis to ensure flight hardware meets reliability and performance targets. 
  • Analyze designs and test results to identify potential failure modes and mitigations. 
  • Collaborate with design engineers to implement design for reliability best practices early in design. 
  • Act as a key stakeholder in reviewing and approving designs for release. 
  • Participate in design reviews and failure mode effects analysis (FMEA) to assess potential reliability issues. 
  • Investigate manufacturing non-conformances and field hardware failures to determine root cause. 
  • Travel as needed to perform deep dives into supplier processes. 
  • Develop and recommend corrective actions to address identified reliability issues. 
  • Utilize reliability modeling and simulation tools to predict system performance and lifespan. 
  • Stay current with industry trends, advancements, and best practices in hardware reliability engineering. 
  • Propose and implement process improvements to drive improvements in reliability across the program. 
Required qualifications:
  • Technical background in materials science, electronics manufacturing processes (PCB fabrication and assembly), hardware reliability concepts, and environmental test practices. 
  • Candidate must be self-starting and come with a growth mindset, with excellent problem-solving skills and attention to detail. 
  • Bachelor's degree in Materials Engineering preferred. Degrees in Mechanical OR Reliability Engineering also are acceptable.  
  • Familiarity with electronics (PCBs, PCBAs, electronic components) and moving mechanical assemblies. 
  • Team-player with clear and concise written and oral communication skills. 
  • Experience working with interdisciplinary teams to execute product design from concept to production. 
  • Experience with the project management of technical projects. 
  • Proficiency in Python, including NumPy, Pandas, SciPy, Plotly, and Matplotlib. 
  • Familiarity with relevant industry standards, including IPC, JEDEC, AIAA, AEC, MIL, and SMC standards. 
  • Ability to obtain a S//SAR level security clearance desired.
Preferred qualifications:
  • Master's degree in Materials Engineering preferred. Degrees in Mechanical OR Reliability Engineering also are acceptable.  
  • 3+ years of experience in hardware reliability engineering, preferably with a focus on aerospace or automotive applications. 
  • Proficiency in failure analysis techniques and materials characterization methods 
  • Experience with environmental testing, including temperature cycling, vibration, highly accelerated limit testing (HALT), and highly-accelerated stress screening (HASS). 
  • Familiarity with PCB fabrication, SMT, and polymerics application manufacturing processes 
  • Significant knowledge of reliability engineering principles, methods, and tools 
$158,542 - $237,812 a year
#LI-JM2
#LD

Full-time regular employee offer package:
Pay within range listed + Bonus + Benefits + Equity
 
Temporary employee offer package:
Pay within range listed above + temporary benefits package (applicable after 60 days of employment)
 
Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.
 
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Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know. 
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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