Remote | Member of Technical Staff, Frontier AI — $600,000–$2,000,000/year

We are sharing a specialised full-time opportunity for experienced technical professionals to operate at the intersection of AI research, machine-learning data systems, evaluation, and real-world model performance.

Selected professionals will take ownership of research and evaluation initiatives designed to generate high-quality, defensible research signal and translate that signal into measurable improvements in AI systems. The role combines evaluation design, ML-oriented data development, failure analysis, quality calibration, and close collaboration with researchers, domain experts, and operational teams.

Key Responsibilities

Research Evaluation & Signal Quality

• 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 whether datasets, experiments, and conclusions meet appropriate quality thresholds
• Act as a quality gate when signal strength, data integrity, or supporting evidence is insufficient

ML-Oriented Data & Evaluation Design

• 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 gaps in evaluation or dataset coverage and recommend where additional investment or iteration is needed
• Develop quality-assurance processes that maintain strong and consistent research standards

Failure Analysis & Iterative Model Improvement

• Investigate model and system behaviour to identify recurring weaknesses and performance limitations
• Iterate rapidly 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 rather than isolated components

Research Collaboration & Technical Communication

• Work closely with researchers, domain experts, operators, and cross-functional teams throughout project kickoff, calibration, and iteration
• Communicate research findings, trade-offs, limitations, and signal strength clearly to technical and non-technical stakeholders
• Translate research progress into credible narratives grounded in evidence
• Support alignment between experimental work and real-world system requirements
• Contribute strong technical judgement in ambiguous, high-impact research environments

Ideal Profile

• Strong professional judgement regarding research signal quality and whether findings are ready to support broader conclusions
• 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

Engagement Details

• Full-time engagement
• Fully remote
• Compensation: $600,000–$2,000,000/year
• Work will span research evaluation, ML-oriented data design, failure analysis, quality calibration, and iterative AI-system improvement
• Responsibilities may include acting as a quality gate for research claims, datasets, and evaluation results
• Collaboration will involve researchers, domain experts, operational teams, and other technical stakeholders
• Project priorities, evaluation frameworks, and research directions may evolve based on experimental findings and system performance
• Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party

About the Platform

This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

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