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. By submitting this application, you acknowledge that your information may be processed by 24-MAG LLC for recruitment and opportunity matching in accordance with our Privacy Policy: https://www.24-mag.com/privacy-policy