1

Internship Machine Learning Engineer Jobs in Nevada

We are looking for a Machine Learning Systems Engineer to join our ML Acceleration team. In this role, you will be responsible for the core systems that enable our researchers to train frontier ...

We are looking for a Machine Learning Systems Engineer to join our ML Acceleration team. In this role, you will be responsible for the core systems that enable our researchers to train frontier ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Certifications in artificial intelligence, machine learning, or cloud platforms, such as AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer, Microsoft ...

... machine learning models and large language models. • Conduct research to provide technical ... & DevOps teams, Data scientists, Machine Learning & GenAI Engineers, and Business teams to pilot ...

... machine learning models and large language models. • Conduct research to provide technical ... & DevOps teams, Data scientists, Machine Learning & GenAI Engineers, and Business teams to pilot ...

Lead Artificial Intelligence Engineer

Las Vegas, NV · On-site

$99K - $130.40K/yr

Machine Learning & Modeling * Supervised, unsupervised, reinforcement learning * Deep learning ... AI Engineering & MLOps * Model training, deployment, monitoring, and retraining * Feature stores ...

next page

Showing results 1-20

Internship Machine Learning Engineer information

What are the key skills and qualifications needed to thrive as an Internship Machine Learning Engineer, and why are they important?

To excel as an Internship Machine Learning Engineer, you typically need a solid background in mathematics, programming (especially Python), and foundational machine learning concepts, often supported by coursework or relevant project experience. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is common, along with proficiency in data processing libraries. Curiosity, strong problem-solving abilities, and effective teamwork and communication skills help set candidates apart. These competencies ensure you can contribute meaningfully to projects, adapt to new challenges, and collaborate productively in a rapidly evolving technical environment.

What types of projects and responsibilities can I expect as an Internship Machine Learning Engineer?

As an Internship Machine Learning Engineer, you will typically support the development, testing, and deployment of machine learning models under the guidance of senior engineers. Your responsibilities may include data preprocessing, exploratory data analysis, implementing algorithms, and evaluating model performance. You'll often collaborate closely with data scientists, software engineers, and product managers, gaining exposure to real-world workflows and tools. This hands-on experience is invaluable for building technical skills and understanding how machine learning solutions are integrated into larger products.

What does an Internship Machine Learning Engineer do?

An Internship Machine Learning Engineer works alongside experienced engineers to help develop, test, and deploy machine learning models. Their responsibilities may include cleaning and preparing data, writing code for model training, evaluating model performance, and contributing to research tasks. Interns often learn to use popular frameworks such as TensorFlow or PyTorch and gain hands-on experience with real-world datasets. This role is designed to help students or recent graduates apply their academic knowledge to practical problems while developing industry-relevant skills.

What is the difference between Internship Machine Learning Engineer vs Data Scientist Intern?

AspectInternship Machine Learning EngineerData Scientist Intern
Required CredentialsBasic programming, introductory ML knowledgeStatistics, data analysis, programming
Work EnvironmentDeveloping ML models, coding, testingData analysis, visualization, reporting
Employer & Industry UsageTech companies, startups, AI firmsTech, finance, healthcare, consulting

Internship Machine Learning Engineers focus on developing and testing machine learning models, often requiring programming and basic ML knowledge. Data Scientist Interns analyze data, create visualizations, and generate insights. Both roles are common in tech and data-driven industries, but ML Engineer internships emphasize model deployment, while Data Science internships focus on data analysis and reporting.

What are the most commonly searched types of Machine Learning Engineer jobs in Nevada? The most popular types of Machine Learning Engineer jobs in Nevada are:
What cities in Nevada are hiring for Internship Machine Learning Engineer jobs? Cities in Nevada with the most Internship Machine Learning Engineer job openings:
Infographic showing various Internship Machine Learning Engineer job openings in Nevada as of May 2026, with employment types broken down into 76% Full Time, 17% Part Time, 5% Contract, and 2% Nights. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Adversarial Machine Learning Engineer

C-Serv

Las Vegas, NV • On-site

Full-time

Medical, Life

Posted 5 days ago


Job description

The Opportunity

We are building a dedicated AI Red Team to rigorously test and harden enterprise-scale AI products.

We are looking for an adversarial machine learning specialist who thinks like an attacker.

This role focuses on identifying vulnerabilities in LLM-driven systems, breaking model guardrails, exploiting data pathways, and stress-testing AI deployments before they reach enterprise customers.

This is a hands-on technical role at the core of AI security.

What You’ll Do
  • Conduct adversarial testing across LLM and AI-based systems
  • Execute real-world attack simulations, including:
  • Prompt injection
  • Jailbreaking and guardrail bypass
  • Data exfiltration attempts
  • Model inversion and evasion techniques
  • RAG manipulation
  • Develop scripts and tooling to automate attack scenarios
  • Analyse model behaviour under adversarial pressure
  • Identify systemic vulnerabilities in:
  • APIs
  • Embedding pipelines
  • Vector databases
  • Fine-tuned model implementations
  • Collaborate with engineering teams to validate remediation
  • Document findings clearly and concisely

You will help ensure AI systems are resilient before they are deployed at scale.

Requirements

What We’re Looking ForCore Technical Skills
  • Strong experience in adversarial ML or AI security research
  • Experience working with LLM-based systems (OpenAI, Anthropic, open-source models, etc.)
  • Deep understanding of:
  • Prompt injection techniques
  • Model jailbreak methodologies
  • AI system exploitation vectors
  • Strong Python skills
  • Experience building custom attack tooling or experimentation frameworks
AI Systems Knowledge
  • Familiarity with:
    • RAG architectures
    • Vector databases
    • Model fine-tuning workflows
    • API-based model deployments
    • Understanding of model safety mechanisms and guardrails
Nice to Have
  • Background in cybersecurity or penetration testing
  • Familiarity with OWASP LLM Top 10
  • Experience working in enterprise environments
Who You Are
  • Curious and relentless
  • Comfortable thinking like an attacker
  • Creative in finding non-obvious vulnerabilities
  • Detail-oriented but fast-moving
  • Comfortable operating in ambiguity
  • Independent but collaborative

You don’t just run test cases — you design new ones.

Benefits

  • Comprehensive Private Medical Coverage
  • Support for Mental Health Expenses
  • Life Insurance Options
  • Attractive Compensation Package