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Machine Learning Security Intern Jobs (NOW HIRING)

Syntiant Corp., a leader in the high-growth AI software and semiconductor solutions space, is looking for a Machine Learning Audio Intern to take on a critical role to enhance our AI Model for Turkey ...

Syntiant Corp., a leader in the high-growth AI software and semiconductor solutions space, is looking for a Machine Learning Audio Intern to take on a critical role to enhance our AI Model for Turkey ...

About Us SentinelOne is a company at the intersection of AI and security, pioneering a new ... As an AI/Machine Learning Engineer Intern , you will be tasked with applying software engineering ...

Machine Learning & NLP: Solid understanding of Large Language Models (LLMs), natural language ... Data Security & Privacy: An understanding of best practices for deploying ML models locally or ...

Reporting to the Manager, Information Security Office, the Information Security Intern is ... Adept at understanding technical information and learning new concepts * Independent worker with a ...

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Machine Learning Security Intern information

See salary details

$25.5K

$42.6K

$88K

How much do machine learning security intern jobs pay per year?

As of Jun 7, 2026, the average yearly pay for machine learning security intern in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What is the difference between Machine Learning Security Intern vs Data Security Intern?

AspectMachine Learning Security InternData Security Intern
Required CredentialsEnrolled in Computer Science, Cybersecurity, or related fields; knowledge of machine learning and security conceptsEnrolled in Cybersecurity, Information Technology, or related fields; understanding of data protection and security protocols
Work EnvironmentTech companies, research labs, or startups focusing on AI and securityOrganizations handling sensitive data, financial institutions, or tech firms with data security teams
Employer & Industry UsagePrimarily in AI, cybersecurity, and tech industriesAcross finance, healthcare, and tech sectors emphasizing data privacy

The Machine Learning Security Intern focuses on securing AI models and algorithms, combining machine learning knowledge with security practices. In contrast, the Data Security Intern concentrates on protecting data assets and ensuring compliance. Both roles require cybersecurity fundamentals but differ in their technical focus and industry applications.

What is a Machine Learning Security Intern?

A Machine Learning Security Intern is a student or early-career professional who assists in protecting machine learning systems and data from security threats. Their responsibilities typically include identifying vulnerabilities in ML models, evaluating attack surfaces, and helping develop secure AI solutions. Interns may also support the implementation of best practices in data privacy, adversarial defense, and secure deployment of ML algorithms. This role provides hands-on experience at the intersection of cybersecurity and artificial intelligence, preparing interns for a career in either or both fields.

What are the key skills and qualifications needed to thrive as a Machine Learning Security Intern, and why are they important?

To thrive as a Machine Learning Security Intern, you need a solid understanding of machine learning concepts, cybersecurity fundamentals, and programming skills, often supported by coursework in computer science or related fields. Familiarity with tools such as Python, TensorFlow or PyTorch, and security analysis platforms is typically expected. Strong analytical thinking, attention to detail, and effective communication help interns collaborate and solve complex security challenges. These skills and qualities are crucial to effectively identify, analyze, and mitigate security risks in machine learning systems.

How do Machine Learning Security Interns typically collaborate with engineering and data science teams during their internship?

Machine Learning Security Interns often work closely with both engineering and data science teams to identify security vulnerabilities in machine learning models and pipelines. Collaboration usually involves participating in code reviews, contributing to threat modeling sessions, and helping to develop or test security tools and protocols. Interns may also assist in analyzing datasets for potential biases or data poisoning threats, and present their findings to cross-functional teams. This teamwork helps interns gain practical experience while ensuring that security best practices are integrated into the development lifecycle.
Infographic showing various Machine Learning Security Intern job openings in the United States as of May 2026, with employment types broken down into 17% Internship, 50% Full Time, and 33% Part Time. Highlights an 94% Physical, 2% Hybrid, and 4% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Machine Learning Applications & Benchmarking Intern

Tenstorrent University Jobs

Santa Clara, CA โ€ข On-site

Other

Posted 27 days ago


Job description

As a Machine Learning Applications & Benchmarking Intern, your role will focus on benchmarking machine learning models on various hardware, creating clean code demos, building end-to-end applications, and ensuring the reliability of our software releases.

This role is on-site based in Santa Clara, CA.
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Who You Are:
  • Background in computer science, ML, or a related field.
  • Proficient in Python with experience using PyTorch or TensorFlow.
  • Comfortable writing clean, reproducible code and debugging performance issues.
  • Curious, collaborative, and always looking to learn something new.

What We Need:

  • Benchmark ML models across hardware setups and analyze performance.
  • Identify slowdowns and optimize for speed and efficiency.
  • Build demos and apps that highlight real ML use cases on our hardware.
  • Contribute to software testing and release validation.

What You Will Learn:

  • Real-world ML benchmarking workflows from model to hardware.
  • Performance tuning and optimization techniques.
  • How ML applications are tested, validated, and productized.
  • Collaboration across software, hardware, and research teams.

Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.