ZipRecruiter

Log In

1

Ai Reliability Engineer Jobs in Nevada (NOW HIRING)

Pwc

US Tech - AI Engineering Manager

Las Vegas, NV · On-site

$73K - $244K/yr

... AI solutions. Responsibilities - Mentor junior engineers and foster their growth - Maintain ... reliability and security - Working with product owners, UX, data science, and security teams to ...

Addison Group

Full Stack Software Engineer

Reno, NV · On-site

$50 - $60/hr

Full Stack Software Engineer developing and maintaining internal systems built on .NET and Angular ... performance, reliability, and scalability gaps across assigned services * Leverage AI-assisted ...

Addison Group

Full Stack Software Engineer

Reno, NV · On-site

$50 - $60/hr

... reliability of the systems you help build. The ideal candidate is a self-motivated engineer who writes clean, well-tested code and embraces modern development practices - including the use of AI ...

Fusion HCR

Sr Data Engineer

Las Vegas, NV

$97K - $131K/yr

Senior Data Engineer Location: Las Vegas, NV Work Arrangement: 100% Onsite - 5 days per week ... reliability * Design and evolve data architecture supporting analytics, BI, and future AI/ML ...

MGM Resorts International

Lead Data Engineer

Las Vegas, NV · On-site

$121K - $162K/yr

... reliability, and cost efficiency. * Develop and maintain ELT/ETL frameworks, data models, and ... Support AI, machine learning, Generative AI, and RAG solutions through scalable data engineering ...

previous pagenext page

Showing results 21-40

All JobsAi Reliability Engineer JobsAi Reliability Engineer Jobs in Nevada

Ai Reliability Engineer information

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 Nevada? For Ai Reliability Engineer jobs in Nevada, the most frequently searched job titles are:
What job categories do people searching Ai Reliability Engineer jobs in Nevada look for? The top searched job categories for Ai Reliability Engineer jobs in Nevada are:
What cities in Nevada are hiring for Ai Reliability Engineer jobs? Cities in Nevada with the most Ai Reliability Engineer job openings:
Ai Reliability Engineer jobs near you
Infographic showing various Ai Reliability Engineer job openings in Nevada as of August 2026, with employment types broken down into 75% Full Time, 21% Part Time, and 4% Contract. Highlights an 72% Physical, 3% Hybrid, and 25% Remote job distribution.

AI Training Specialist - Life Sciences

micro1 AI

Reno, NV • Remote

$90 - $120/hr

Part-time

Posted 9 days ago

Job description

Role Title: Bioinformatics Scientist


Role Type: Contractor


Location: Remote


micro1 is engaging Bioinformatics Scientists to contribute their specialized expertise to a customer's innovative project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Analyze complex datasets related to medicinal chemistry using advanced bioinformatics methodologies.
  2. Provide detailed scientific input and content to support the development and training of AI models.
  3. Curate, annotate, and validate datasets relevant to drug discovery and molecular analysis.
  4. Evaluate and synthesize findings from biological, chemical, and clinical data sources.
  5. Offer subject matter expertise on experimental design and data interpretation within medicinal chemistry.
  6. Assess AI-generated outputs for scientific accuracy, relevance, and reliability.
  7. Deliver comprehensive written feedback and actionable recommendations for model improvement.


Preferred Qualifications

  1. Advanced degree (e.g., PhD or MSc) in Bioinformatics, Computational Biology, Medicinal Chemistry, or a related discipline.
  2. In-depth knowledge of medicinal chemistry concepts, including structure-activity relationships and drug design principles.
  3. Demonstrated experience in handling and interpreting large-scale omics or cheminformatics datasets.
  4. Familiarity with software tools, databases, and programming languages commonly used in bioinformatics (e.g., Python, R, RDKit, KNIME).
  5. Strong scientific communication skills, with the ability to clearly articulate complex ideas and technical concepts.
  6. Proven track record of contributing to research projects at the intersection of biology, chemistry, and data science.
  7. Experience collaborating in multidisciplinary or remote project environments is advantageous.

Apply