FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing datasets and evaluation frameworks for AI systems, with a strong focus on translating operational issues into structured research problems. Proven ability to collaborate across diverse teams and communicate complex findings effectively to both technical and non-technical stakeholders.
Highest-signal resume keywords
Master's Degree In Computer ScienceDataset DesignEvaluation FrameworksAnalytical SkillsAI System Evaluation
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningArtificial IntelligenceReinforcement LearningData CurationExperimental AnalysisQuality Assurance ProcessesTechnical AnalysisBenchmarkingTool DevelopmentSystem Evaluation
Soft Skills
Strong Communication SkillsCollaborative ExperienceJudgement Regarding Research-Signal QualityAbility To Translate Ambiguous IssuesProven Execution In High-Ambiguity Environments
Industry Keywords
Enterprise AI WorkflowsAgentic SystemsResearch CollaboratorTechnical ResearchOperational IssuesSystem BehaviourFailure ModesRapid Iteration CyclesClient-Facing ExperienceOpen Research Initiatives
About the role
Key responsibilities & impact- 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 analyses of system behaviour, limitations, and opportunities for improvement
- Design high-signal datasets targeting model and system weaknesses
- Develop evaluation protocols, quality criteria, and structured assessment frameworks
- Identify gaps in existing datasets and evaluation coverage
- 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
- Analyse 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 to reports, benchmarks, evaluation documentation, and technical analyses
- Communicate complex findings to technical and non-technical stakeholders
Requirements
What you’ll need- 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
Benefits
Comp & perks- Fully remote work arrangement
- Full-time engagement
- Compensation of $300,000–$700,000/year
- Remote consulting opportunities through 24-MAG LLC