Remote | Member of Technical Staff, Enterprise AI — $300,000–$700,000/year

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

• Embed within enterprise AI workflows as a technical research collaborator
• Work alongside domain experts and enterprise teams to understand real-world system behaviour
• Identify, formalise, and prioritise failure modes emerging from deployed AI systems
• Translate operational issues into structured research questions and measurable technical problems
• Produce clear analyses of system behaviour, limitations, and opportunities for improvement

ML-Oriented Data & Evaluation Design

• Design high-signal datasets targeting identified model and system weaknesses
• Develop evaluation protocols, quality criteria, and structured assessment frameworks
• Apply strong judgement to data selection, evaluation design, and research-signal quality
• Identify gaps in existing datasets and evaluation coverage
• Structure research workflows to support measurable improvements in model performance

Experimentation & Agentic Workflow Evaluation

• Run rapid experimental cycles to test hypotheses and quantify system improvements
• Develop and benchmark agentic workflows with a focus on robustness, reliability, and scalability
• Evaluate AI systems operating across complex enterprise workflows
• Analyse experimental results and determine whether observed improvements are meaningful and reproducible
• Iterate on datasets, evaluations, and system configurations based on research findings

Research Tooling & Cross-Functional Collaboration

• Build lightweight tooling to support evaluation, data curation, experimentation, and rapid iteration
• Collaborate across research, engineering, product, domain, and enterprise-facing teams
• Translate research findings into clear, decision-oriented recommendations
• Contribute to research artifacts including reports, benchmarks, evaluation documentation, and technical analyses
• Communicate complex findings clearly to both technical and non-technical stakeholders

Ideal Profile

• Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a closely related technical discipline
• Strong judgement regarding research-signal quality, data selection, and evaluation design
• Experience designing datasets, evaluation frameworks, or QA processes for machine-learning systems
• Ability to translate ambiguous operational issues into structured research and evaluation problems
• Familiarity with reinforcement-learning environments, agentic systems, or AI-system evaluation
• Strong analytical skills and ability to produce concise, actionable technical insights
• Proven ability to execute effectively within rapid iteration cycles and high-ambiguity environments
• Strong written and verbal communication skills
• Collaborative experience across research, product, engineering, and domain teams
• Client-facing experience within technical or research-focused environments is advantageous
• Experience building internal research or evaluation tooling is beneficial
• Contributions to benchmarks, research publications, or open research initiatives are advantageous
• Exposure to enterprise AI deployments or forward-deployed research environments is strongly valued

Engagement Details

• Full-time engagement
• Fully remote
• Compensation: $300,000–$700,000/year
• Work will span enterprise AI research, evaluation design, ML-oriented data systems, experimentation, and agentic workflow analysis
• Responsibilities will involve direct collaboration with research, product, technical, domain, and enterprise stakeholders
• Research priorities, datasets, evaluation frameworks, and system requirements may evolve based on experimental findings and deployment needs
• 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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