We are sharing a specialised full-time opportunity for experienced technical professionals to operate at the intersection of enterprise AI, applied research, machine-learning evaluation, and real-world AI system performance.
Selected professionals will work directly within enterprise AI workflows to identify real-world failure modes, design high-signal datasets and evaluation frameworks, and run rapid experimental cycles that improve system performance. The role combines forward-deployed research, ML-oriented data design, agentic workflow evaluation, technical analysis, and close collaboration across research, product, domain, and enterprise teams.
Key Responsibilities
Enterprise AI Research & Failure Analysis
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Embed within enterprise AI workflows as a technical research collaborator
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Work alongside domain experts and enterprise teams to understand real-world system behaviour
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Identify, formalise, and prioritise failure modes emerging from deployed AI systems
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Translate operational issues into structured research questions and measurable technical problems
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Produce clear analyses of system behaviour, limitations, and opportunities for improvement
ML-Oriented Data & Evaluation Design
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Design high-signal datasets targeting identified model and system weaknesses
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Develop evaluation protocols, quality criteria, and structured assessment frameworks
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Apply strong judgement to data selection, evaluation design, and research-signal quality
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Identify gaps in existing datasets and evaluation coverage
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Structure research workflows to support measurable improvements in model performance
Experimentation & Agentic Workflow Evaluation
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Run rapid experimental cycles to test hypotheses and quantify system improvements
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Develop and benchmark agentic workflows with a focus on robustness, reliability, and scalability
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Evaluate AI systems operating across complex enterprise workflows
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Analyse experimental results and determine whether observed improvements are meaningful and reproducible
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Iterate on datasets, evaluations, and system configurations based on research findings
Research Tooling & Cross-Functional Collaboration
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Build lightweight tooling to support evaluation, data curation, experimentation, and rapid iteration
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Collaborate across research, engineering, product, domain, and enterprise-facing teams
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Translate research findings into clear, decision-oriented recommendations
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Contribute to research artifacts including reports, benchmarks, evaluation documentation, and technical analyses
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Communicate complex findings clearly to both technical and non-technical stakeholders
Ideal Profile
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Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a closely related technical discipline
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Strong judgement regarding research-signal quality, data selection, and evaluation design
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Experience designing datasets, evaluation frameworks, or QA processes for machine-learning systems
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Ability to translate ambiguous operational issues into structured research and evaluation problems
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Familiarity with reinforcement-learning environments, agentic systems, or AI-system evaluation
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Strong analytical skills and ability to produce concise, actionable technical insights
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Proven ability to execute effectively within rapid iteration cycles and high-ambiguity environments
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Strong written and verbal communication skills
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Collaborative experience across research, product, engineering, and domain teams
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Client-facing experience within technical or research-focused environments is advantageous
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Experience building internal research or evaluation tooling is beneficial
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Contributions to benchmarks, research publications, or open research initiatives are advantageous
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Exposure to enterprise AI deployments or forward-deployed research environments is strongly valued
Engagement Details
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Full-time engagement
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Fully remote
- Compensation: $300,000–$700,000/year
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Work will span enterprise AI research, evaluation design, ML-oriented data systems, experimentation, and agentic workflow analysis
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Responsibilities will involve direct collaboration with research, product, technical, domain, and enterprise stakeholders
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Research priorities, datasets, evaluation frameworks, and system requirements may evolve based on experimental findings and deployment needs
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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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