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
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Own research and evaluation initiatives from problem framing through data design, quality calibration, and signal validation
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Define rigorous approaches for determining whether experimental results provide reliable and defensible research signal
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Analyse model and system failures to identify root causes, edge cases, and opportunities for improvement
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Evaluate whether datasets, experiments, and conclusions meet appropriate quality thresholds
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Act as a quality gate when signal strength, data integrity, or supporting evidence is insufficient
ML-Oriented Data & Evaluation Design
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Design ML-oriented data systems including task definitions, annotation schemas, rubrics, incentives, and supporting pipelines
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Structure data and evaluation workflows around downstream model-performance objectives
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Translate ambiguous real-world behaviour into measurable evaluation frameworks and new data categories
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Identify gaps in evaluation or dataset coverage and recommend where additional investment or iteration is needed
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Develop quality-assurance processes that maintain strong and consistent research standards
Failure Analysis & Iterative Model Improvement
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Investigate model and system behaviour to identify recurring weaknesses and performance limitations
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Iterate rapidly on evaluations, datasets, feedback loops, and quality standards
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Use experimental findings to guide improvements in model or agent performance
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Determine when research directions should be expanded, revised, paused, or discontinued based on evidence
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Maintain a systems-level perspective focused on end-to-end AI performance rather than isolated components
Research Collaboration & Technical Communication
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Work closely with researchers, domain experts, operators, and cross-functional teams throughout project kickoff, calibration, and iteration
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Communicate research findings, trade-offs, limitations, and signal strength clearly to technical and non-technical stakeholders
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Translate research progress into credible narratives grounded in evidence
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Support alignment between experimental work and real-world system requirements
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Contribute strong technical judgement in ambiguous, high-impact research environments
Ideal Profile
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Strong professional judgement regarding research signal quality and whether findings are ready to support broader conclusions
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Experience designing ML-oriented datasets, evaluation frameworks, annotation systems, rubrics, or QA processes
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Ability to translate complex and ambiguous real-world system behaviour into structured research and evaluation opportunities
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Strong ownership mindset and comfort making decisions in uncertain or rapidly evolving environments
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Excellent written and verbal communication skills
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Ability to explain technical trade-offs, limitations, evidence quality, and research findings clearly
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Proven experience working directly with researchers, technical experts, or domain specialists during project calibration and iteration
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Systems-level understanding of model, agent, or AI-system performance
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Experience with reinforcement-learning environments, simulators, or feedback-driven training systems is advantageous
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Experience improving agentic systems or AI systems operating within real-world workflows is beneficial
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Prior work within applied research or production environments with direct impact on deployed systems is advantageous
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Experience designing evaluations for complex or real-world tasks is strongly valued
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Familiarity with expert incentive design or high-stakes technical research programmes is beneficial
Engagement Details
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Full-time engagement
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Fully remote
- Compensation: $600,000–$2,000,000/year
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Work will span research evaluation, ML-oriented data design, failure analysis, quality calibration, and iterative AI-system improvement
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Responsibilities may include acting as a quality gate for research claims, datasets, and evaluation results
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Collaboration will involve researchers, domain experts, operational teams, and other technical stakeholders
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Project priorities, evaluation frameworks, and research directions may evolve based on experimental findings and system performance
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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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