Remote | Member of Technical Staff, Enterprise AI — $300,000–$700,000/year
About The Position
This full-time, fully remote role operates at the intersection of enterprise AI, applied research, machine-learning evaluation, and real-world AI system performance. The selected professional will work directly within enterprise AI workflows to identify real-world failure modes, design high-signal datasets and evaluation frameworks, and conduct rapid experiments to improve system performance. The role combines forward-deployed research, ML-oriented data design, agentic workflow evaluation, technical analysis, and collaboration with research, product, domain, and enterprise teams. The work spans enterprise AI research, evaluation design, ML-oriented data systems, experimentation, and agentic workflow analysis. It involves direct collaboration with research, product, technical, domain, and enterprise stakeholders. Research priorities, datasets, evaluation frameworks, and system requirements may evolve in response to experimental findings and deployment needs. Work must not use confidential or proprietary information belonging to any employer, client, institution, or other third party. The opportunity is available through 24-MAG LLC, a platform that connects experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
Requirements
- Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a closely related technical discipline.
- Strong judgment 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.
- 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.
Nice To Haves
- Client-facing experience in technical or research-focused environments.
- Experience building internal research or evaluation tooling.
- Contributions to benchmarks, research publications, or open research initiatives.
- Exposure to enterprise AI deployments or forward-deployed research environments is strongly valued.
Responsibilities
- Embed within enterprise AI workflows as a technical research collaborator.
- Work with domain experts and enterprise teams to understand real-world system behavior.
- Identify, formalize, and prioritize failure modes in deployed AI systems.
- Translate operational issues into structured research questions and measurable technical problems.
- Produce clear analyses of system behavior, limitations, and opportunities for improvement.
- Design high-signal datasets targeting identified model and system weaknesses.
- Develop evaluation protocols, quality criteria, and structured assessment frameworks.
- Apply judgment 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.
- Run rapid experimental cycles to test hypotheses and quantify system improvements.
- Develop and benchmark agentic workflows for robustness, reliability, and scalability.
- Evaluate AI systems operating across complex enterprise workflows.
- Analyze experimental results and determine whether improvements are meaningful and reproducible.
- Iterate on datasets, evaluations, and system configurations based on research findings.
- Build lightweight tooling for 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 reports, benchmarks, evaluation documentation, and technical analyses.
- Communicate complex findings to technical and non-technical stakeholders.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
