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Ai Safety Internship Jobs (NOW HIRING)

Peraton

Generative AI Engineering Intern (Graduate)

$17.25 - $22.25/hr

Interns can support 100% remotely. This open-ended graduate internship is designed to provide ... Familiarity with responsible AI principles, AI safety, and techniques for mitigating bias and ...

CACI International, Inc.

AI Prompt Engineer

Ashburn, VA · On-site

... experience , internships, or relevant academic project work. * 1+ year of hands-on experience ... Understanding of AI safety considerations (e.g., prompt injection, bias, data privacy) * Exposure ...

Alinia AI

Applied AI Research Intern

At Alinia, our Applied AI Research Interns play a pivotal role in exploring new possibilities and ... Cutting-edge tech: Work on one of the most important challenges in AI--alignment, safety, and trust

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How much do ai safety internship jobs pay per hour?

As of Aug 14, 2026, the average hourly pay for ai safety internship in the United States is $17.87, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $21.15 per hour, depending on experience, location, and employer.

What is an AI safety internship?

An AI Safety Internship is a temporary position, usually for students or recent graduates, focused on researching and developing methods to ensure artificial intelligence systems are safe, reliable, and aligned with human values. Interns typically work on projects that might include risk assessment, robustness testing, interpretability, or ethical implications of AI technologies. These internships are often offered by universities, research institutes, or tech companies and provide hands-on experience in a rapidly growing field. Participants gain exposure to cutting-edge research and contribute to solving some of the most important challenges in AI development.

What types of projects does an AI safety intern typically work on?

As an AI Safety Intern, you can expect to contribute to projects like developing tools for identifying and mitigating risks in machine learning models, performing literature reviews on safety methodologies, and assisting with experiments to test model robustness. You may collaborate with research scientists, engineers, and other interns to brainstorm solutions and analyze results. The work often involves both independent tasks and team-based problem-solving, offering exposure to real-world challenges in ensuring AI systems operate safely and ethically.

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

To thrive as an AI Safety Intern, you typically need a strong background in computer science, mathematics, or a related field, along with foundational knowledge of machine learning and ethics. Familiarity with programming languages like Python, machine learning frameworks (such as TensorFlow or PyTorch), and experience with relevant research tools are often required. Critical thinking, effective communication, and a proactive learning attitude set standout candidates apart in this rapidly evolving area. These skills and qualities are crucial for contributing to responsible AI development and addressing complex safety challenges in the field.
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Ai Safety Internship jobs near you
Infographic showing various Ai Safety Internship job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 79% Full Time, 17% Part Time, 2% Contract, and 1% Nights. Highlights an 99% Physical, and 1% Remote job distribution, with an average salary of $37,171 per year, or $17.9 per hour.

AI Engineer, Internship - Summer 2026 - Applications Open Now

Postman

Berkeley, CA

Temporary, Internship

Posted 11 days ago

Job description

The Opportunity (Summer 2026 AI Internship - Applications Open Now)

We're seeking an AI Engineer Intern to work alongside our AI team on large-scale AI and Agentic  systems from data pipeline to production deployment. This role is scoped for someone with foundational experience who wants to deepen it: you'll own discrete pieces of real systems under the mentorship of senior engineers, not shadow work or isolated coursework-style projects.

What You'll Do

You'll work directly with the AI team, taking responsibility for well-scoped pieces of real systems, with mentorship from senior engineers.

Benchmarks & Evaluation

  • Contribute to APIFlow-Bench, our open-source benchmark for real API-development work: design and review benchmark tasks and their mock API environments, extend the evaluation harness and task-generation pipeline in Python, and help maintain the public multi-model leaderboard with statistical confidence intervals.
  • Help build a new action-level AI safety benchmark: instead of grading what a model says, it scores what an agent actually does inside a simulated enterprise API environment. You'll work on scenario design, threat modeling (prompt injection, data exfiltration, permission overreach), and auditable evaluation design.

Model Training & Efficiency

  • Fine-tune open-weight models for tool calling and agentic tasks (SFT, distillation, and RL) using PyTorch and the open-source training ecosystem, on both managed training platforms and self-managed cloud GPUs.
  • Design and run experiments with rigor: evaluate every training run on our benchmarks, support ablation studies and error analysis, track experiments, and report results honestly, including cost.
  • Evaluate ultra-low-bit quantized models for on-device use: extend our quantized vs. full-precision benchmark comparisons and analyze where and why they diverge.

Agent Systems & Engineering Practice

  • Help build the next generation of Postman's in-product AI agent (Agent Mode): a deliberately minimal agent architecture that calls LLM APIs directly (tool loops, multi-step execution, checkpointing), primarily in TypeScript. No prior TypeScript is required; strong Python fundamentals transfer quickly.
  • Read the source code of open-source agent harnesses and turn what you learn into design specs and prototypes.
  • Document experiments, design decisions, and runbooks so your work is legible to the next person; flag safety, fairness, or privacy concerns you observe in model or agent behavior.
About You
  • Currently pursuing a BS, MS, or PhD in Computer Science, Data Science, or a related quantitative field.
  • Hands-on experience training or evaluating ML models: course projects, research, hackathons, or a prior internship all count.
  • Solid Python fundamentals: data structures, functions, basic testing; comfortable writing and reviewing code outside of notebooks.
  • Working knowledge of at least one deep-learning framework (PyTorch preferred).
  • Clear written and verbal communication, and a habit of documenting what you build.
Preferred Qualifications  (none required; the more of these you have, the better)
  • Experience fine-tuning open-weight LLMs (SFT, LoRA, RL, or distillation), with the improvement measured on a benchmark.
  • Experience building LLM agents (tool calling, multi-step loops) or LLM evaluation harnesses/benchmarks, and reporting results with statistical rigor.
  • A track record of shipping real software end-to-end: APIs and services, CLIs, Docker, CI/CD, cloud; public code on GitHub is a big plus.
  • Interest or experience in AI safety and robustness: red-teaming, prompt injection, agent security, fairness, or interpretability.
  • Exposure to model-efficiency work: quantization, low-bit inference, or serving optimization.
  • Evidence of rigor and initiative: publications, technical blog posts, ablation studies, or self-driven side projects with quantified results.
  • Fluency with AI coding tools (Claude Code, Cursor, Codex) to ship fast while still deeply understanding the systems you build.

Apply