Member of Technical Staff, Frontier AI

Job not on LinkedIn

🔥 18 hours ago

🗽 New York – Remote

🗽 New York – Remote

💵 $600k - $2M / year

⏰ Full Time

🔴 Lead

🖥 Software Engineer

👻 Ghost score 0%

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

• Own research and evaluation initiatives from problem framing through data design, quality calibration, and signal validation • Define rigorous approaches for determining whether experimental results provide reliable and defensible research signal • Analyse model and system failures to identify root causes, edge cases, and opportunities for improvement • Evaluate datasets, experiments, and conclusions against appropriate quality thresholds • Act as a quality gate when signal strength, data integrity, or supporting evidence is insufficient • Design ML-oriented data systems, including task definitions, annotation schemas, rubrics, incentives, and supporting pipelines • Structure data and evaluation workflows around downstream model-performance objectives • Translate ambiguous real-world behaviour into measurable evaluation frameworks and new data categories • Identify evaluation or dataset coverage gaps and recommend additional investment or iteration • Develop quality-assurance processes that maintain consistent research standards • Investigate model and system behaviour to identify recurring weaknesses and performance limitations • Iterate on evaluations, datasets, feedback loops, and quality standards • Use experimental findings to guide improvements in model or agent performance • Determine when research directions should be expanded, revised, paused, or discontinued based on evidence • Maintain a systems-level perspective focused on end-to-end AI performance • Collaborate with researchers, domain experts, operators, and cross-functional teams during project kickoff, calibration, and iteration • Communicate research findings, trade-offs, limitations, and signal strength to technical and non-technical stakeholders • Translate research progress into evidence-grounded narratives • Support alignment between experimental work and real-world system requirements • Contribute technical judgement in ambiguous, high-impact research environments

🎯 Requirements

• Experienced technical professional • Strong professional judgement regarding research signal quality • Experience designing ML-oriented datasets, evaluation frameworks, annotation systems, rubrics, or QA processes • Ability to translate complex and ambiguous real-world system behaviour into structured research and evaluation opportunities • Strong ownership mindset and comfort making decisions in uncertain or rapidly evolving environments • Excellent written and verbal communication skills • Ability to explain technical trade-offs, limitations, evidence quality, and research findings clearly • Proven experience working directly with researchers, technical experts, or domain specialists during project calibration and iteration • Systems-level understanding of model, agent, or AI-system performance • Experience with reinforcement-learning environments, simulators, or feedback-driven training systems is advantageous • Experience improving agentic systems or AI systems operating within real-world workflows is beneficial • Prior work within applied research or production environments with direct impact on deployed systems is advantageous • Experience designing evaluations for complex or real-world tasks is strongly valued • Familiarity with expert incentive design or high-stakes technical research programmes is beneficial • Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party

🏖️ Benefits

• Fully remote work • Full-time engagement • Remote consulting opportunity • Compensation of $600,000–$2,000,000/year