LLM Red Team & Benchmark Evaluation Specialist

🕒 July 27

🗽 New York – Remote

🗽 New York – Remote

💵 $55 - $85 / hour

⏰ Full Time

🟢 Junior

👻 Ghost score 6%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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24-MAG

WebsiteLinkedInAll Job Openings

2 - 10 employees

🤝 B2B

💼 Consulting

B2B • Consulting

24-MAG is a commercial strategy and execution firm that helps B2B organizations design and implement systems, workflows, and operating rhythms for sales, client management, and cross-functional projects. They focus on transforming scattered processes into aligned, measurable, and scalable commercial functions—covering pipeline structure, account management frameworks, and operational discipline for teams seeking efficient, intentional growth.

📋 Description

• Adversarial Model Evaluation • Probe frontier AI models across coding, machine learning, analysis, and multi-step agentic tasks • Identify subtle errors, vulnerabilities, edge cases, and misleadingly plausible outputs • Investigate situations where models appear capable while reaching incorrect or unsupported conclusions • Design reproducible experiments to isolate and validate model failure modes • Benchmark & Challenge Design • Convert observed model weaknesses into rigorous benchmark tasks • Develop challenges that are technically demanding while remaining fair and objectively assessable • Define clear task requirements, expected outcomes, and evaluation criteria • Ensure tasks require genuine reasoning rather than allowing shortcuts or superficial pattern matching • Failure Analysis & Documentation • Document findings with clear evidence, methodology, and reproducible steps • Explain why a model failed and which capabilities or assumptions contributed to the error • Produce detailed technical write-ups for researchers and task authors • Track recurring failure patterns across models, prompts, and evaluation environments • Task Strengthening & Research Collaboration • Work with task authors to close loopholes, grading gaps, and unintended shortcuts • Review benchmark tasks for ambiguity, exploitability, and evaluation reliability • Share insights with researchers and other specialists to improve benchmark coverage • Participate in iterative calibration, peer review, and task-refinement workflows

🎯 Requirements

• At least 1 year of experience in research, research engineering, security, AI evaluation, or a related technical role • Demonstrated experience identifying vulnerabilities, edge cases, or failure modes in LLMs or ML systems • Background in red teaming, adversarial testing, security research, benchmark development, or rigorous model evaluation • Working proficiency in Python and Git • Ability to develop scripts, probes, and analyses independently • Strong familiarity with LLM capabilities, limitations, and evaluation techniques • Excellent written communication and technical documentation skills • Creativity, precision, and persistence when working through ambiguous research problems • Reliable availability for approximately 35 hours per week • A master's degree or PhD in a STEM field is highly relevant