About the role
We are seeking an analytical researcher and red-teaming specialist to build evidence-based web-research benchmark tasks that reliably expose weaknesses in state-of-the-art (SOTA) AI models (e.g., Claude Opus 5 Max). In this role, you will discover hard-to-surface facts, build precise research tasks around them, test them directly against leading LLMs, and diagnose the exact failure mechanisms in the model's search and reasoning paths. Main tasks listed: Obscure Fact Discovery: Deeply research niche topics to identify factual ground truths that cannot be surfaced via simple, direct search queries; Source Verification: Rigorously verify facts against primary or highly credible sources, cross-check for contradictory evidence, and document exact source URLs; Task Construction: Draft clear, answerable benchmark tasks designed specifically to expose structural weaknesses in model research strategies; Red-Teaming & Model Testing: Execute tasks against designated SOTA models to confirm they reliably trigger intended model failures.
Experience
Core Experience: 1+ years in investigative research, fact-checking, AI benchmark task authoring, red-teaming, or technical QA review; Benchmark Familiarity: Proven experience authoring, testing, or reviewing tasks for AI benchmarks such as GAIA, BrowseComp, SimpleQA, SWE-bench, Terminal-Bench, or Humanity’s Last Exam (HLE); Analytical & OSINT Skills: Intermediate-to-advanced web search and Open Source Intelligence (OSINT) skills; ability to navigate academic archives, local records, or obscure databases; Model Trajectory Debugging: Strong analytical capability to inspect model reasoning chains, search logs, and source selection to diagnose root causes of failure; Precision Writing: Ability to author clear, unambiguous benchmark prompts with bulletproof, primary-sourced answer keys
Selection process
Apply through the official Turing page (free account required). The listing does not describe further selection steps.
Eligibility
Not stated on the listing (remote)