Skills to Put on a Resume for an AI Safety Researcher

The skills real AI Safety Researcher postings screen for

Pulled from our AI Safety Researcher role taxonomy — not a generic list. Each one names what a recruiter reads into it and, more usefully, how to actually back it up.

• Python or JavaScript

  Listing a language only matters if you can point to something it built — recruiters skim past “Python” unless there's a repo or project attached.

  Evidence, not just a bullet: Link a GitHub repo with a script that solves a real problem — data cleaning, an API integration, a small app — with a README that explains what it does.

  Practice it:Audit a Hiring-Screening Model for Demographic BiasDesign a Capability Evaluation for an Open-Weights Coding Model

• PyTorch or TensorFlow

  Naming a specific deep-learning framework — not just “machine learning” — says you’ve actually built and trained a model, not just called an API.

  Evidence, not just a bullet: Link a repo with a model you trained, even on a small dataset, and mention the architecture and one hyperparameter choice you made.

  Practice it:Investigate Why Our Generative Model Memorizes Training DataRun an Adversarial-Robustness Audit on a Face-Liveness Model for a Fintech

• AI Ethics

  Methodology

  AI Ethics is a named method, not a vague competency — claiming it says you can apply a specific, repeatable approach, not just "think analytically."

  Evidence, not just a bullet: Walk through one real case where you applied AI Ethics step by step, including what the output was.

  Practice it:Ethical Stress Test of an AI Tutoring StartupGoing Concern and Evidence Strategy for a UK Retailer

• Git

  Tool

  Not really a differentiator on its own anymore, but a messy Git history (force-pushes over main, no commit messages) is a real red flag recruiters notice.

  Evidence, not just a bullet: Point to a repo with a clean, readable commit history — that's the evidence, more than the word “Git” on a line by itself.

  Practice it:Branch Predictor Study for a Cryptocurrency Hashing Library

• Adversarial robustness research

  Adversarial robustness research is one of the specific, verifiable skills recruiters screen for in this role — naming it plainly is what gets it past both an ATS keyword match and a human skim.

  Evidence, not just a bullet: Don't just list "Adversarial robustness research" — attach one concrete example of using it: a project, a number, or an artifact a recruiter could actually look at.

  Practice it:Run an Adversarial-Robustness Audit on a Face-Liveness Model for a Fintech

Every challenge below is an AI-generated practice brief — not a real client engagement — that produces a submission you can point to as evidence for the skills above.

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  ResearchSeniorNew

  Investigate Why Our Generative Model Memorizes Training Data

  Pick a small open-source diffusion model (e.g., a Stable-Diffusion-class community model trained on LAION-subset). Reproduce a published membership-inference + extraction probe …

  • Generative Models
  • Memorization Analysis
  • Differential Privacy

  Advanced Deep Learning
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  ResearchIntermediateNew

  Safety-Test a Customer-Service Agent for Adversarial Prompts

  You receive a sandboxed instance of the agent (a tool-using LLM that can read account balances and open support tickets — both mocked). Design a red-team suite of at least 80 pr…

  • LLM Agents
  • Red Teaming
  • Adversarial Prompts

  AI Agents and LLM-Based Agents
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  AnalysisBeginnerNew

  Audit a Hiring-Screening Model for Demographic Bias

  You receive: (a) inference API access to the production model (black-box), (b) a 12,000-resume audit benchmark with self-declared gender and age-band labels (consented, GDPR-com…

  • Fairness Metrics
  • Bias Auditing
  • Model Evaluation

  AI Ethics, Fairness, and Responsible AI
• 

  ResearchBeginnerNew

  Case-Study Analysis of a Public AI Incident

  Pick one public AI incident (suggestions: a chatbot's harmful response that went viral, a facial-recognition false-arrest case, a financial-model bias scandal). Produce a 6-page…

  • Incident Analysis
  • Responsible Ai
  • Case Study Research

  AI Ethics, Fairness, and Responsible AI
• 

  ResearchIntermediateNew

  Audit a Public LLM Benchmark for Validity Threats

  Choose one open LLM benchmark (e.g., MMLU, GPQA, BIG-Bench-Hard, MATH). Read the benchmark paper plus at least three follow-up critiques. Audit (1) data contamination risk again…

  • Benchmark Evaluation
  • Data Contamination Analysis
  • Annotation Methodology

  AI Measurement and Evaluation
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  ResearchIntermediateNew

  Red-Team a Customer-Service Chatbot for Jailbreak Resistance

  Use a published taxonomy of jailbreak categories (prompt injection, persona override, encoded payloads, multi-turn escalation, refusal bypass, tool-misuse). For each category, d…

  • Red Teaming
  • Jailbreak Analysis
  • LLM Evaluation

  AI Safety and Alignment