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# Remote

- **Company:** MAG 24 LLC
- **Location:** New York, NY, United States (Remote available)
- **Salary:** $300,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Statistical Hypothesis Testing, Machine Learning, System Testing, Multi-Agent Systems, Information Technology, Deployment Automation, Automation Anywhere, Data Selection
- **Published:** September 17, 2026
- **Apply:** https://www.careerjet.com/jobad/us0af198ee9a091b72f27abf5f358f6fa6

## About the Role

* 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

## Description

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., 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


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