AI Safety Researcher Resume Example

Professional Summary

AI safety researcher with 4+ years working on alignment, interpretability, and robustness of large language models. Published at top venues on topics including reward hacking, adversarial robustness, and scalable oversight, with experience translating safety research into production guardrails at major AI labs.

Key Skills

What to Include on a AI Safety Researcher Resume

• A concise summary that states your ai safety researcher experience level, strongest domain, and the business problems you solve.
• A skills section that mirrors the job description language for Alignment Research, Interpretability, Red Teaming, Adversarial Robustness.
• Experience bullets that connect AI safety, alignment research, interpretability to measurable outcomes such as cost savings, faster delivery, better quality, or improved customer results.
• Tools, platforms, certifications, and methods that are current for ai & machine learning roles.
• Recent projects that show ownership, cross-functional work, and a clear result instead of generic responsibilities.

Sample Experience Bullets

• Published 8 peer-reviewed papers on AI safety topics at NeurIPS, ICML, and FAccT with over 1,500 combined citations, covering alignment techniques, mechanistic interpretability methods, and adversarial robustness of large language models
• Designed the red teaming evaluation framework used to assess 3 major model releases before public deployment, testing across 15 risk categories and identifying over 200 failure modes including jailbreaks, harmful completions, and factual hallucinations
• Built interpretability tools in PyTorch that visualize attention patterns, neuron activation, and feature attribution in transformer architectures. The tools are used by over 20 researchers internally and have been open-sourced with 400 GitHub stars
• Developed an automated evaluation suite that tests model alignment across 1,000 scenarios covering helpfulness, harmlessness, and honesty dimensions. The suite became the standard pre-release safety gate and runs automatically on every model checkpoint
• Contributed to the constitutional AI training methodology by writing evaluation criteria and testing reward model behavior, helping reduce harmful model outputs by 85% on internal benchmarks while maintaining helpfulness scores within acceptable ranges
• Design evaluation benchmarks that measure specific safety properties including truthfulness on factual questions, appropriate refusal on harmful requests, and instruction-following fidelity. Maintain a benchmark suite of 5,000 test cases that is updated quarterly
• Work with the policy and governance team to translate technical safety research findings into deployment guidelines, risk frameworks, and usage policies. Co-authored the model card documentation template that accompanies every public model release
• Ran experiments on reward hacking and specification gaming in reinforcement learning from human feedback, documenting cases where models learned to exploit reward signals in unintended ways. Published the findings with proposed mitigation strategies
• Participate in cross-organizational safety reviews before major model launches, providing technical input on capability evaluations, risk assessments, and recommended mitigations. Reviewed 4 model launches and flagged 2 issues that delayed release until fixes were verified
• Built a dataset of 10,000 adversarial prompts organized by attack category, used for stress-testing model guardrails and training more robust safety classifiers. The dataset is maintained and expanded with new attack patterns discovered through ongoing red team exercises
• Mentored 3 junior researchers on experimental methodology, paper writing, and navigating the peer review process. Helped all 3 publish their first first-author papers at top-tier venues within their first 18 months on the team

ATS Keywords for AI Safety Researcher Resumes

Use these terms naturally where they match your experience and the job description.

Recommended Certifications

• Ph.D. in Computer Science/AI Safety
• Alignment Forum Contributor

What Does a AI Safety Researcher Do?

• Design, develop, and maintain software solutions using Alignment Research, Interpretability, Red Teaming and related technologies
• Collaborate with cross-functional teams including product managers, designers, and QA engineers to deliver features on schedule
• Write clean, well-tested code following industry best practices for AI safety and alignment research
• Participate in code reviews, technical discussions, and architecture decisions to improve system quality and team knowledge
• Troubleshoot production issues, optimize performance, and ensure system reliability across all environments

Resume Tips for AI Safety Researchers