Staff Research Engineer – AI & Machine Learning

Gramian Consulting

. Investigate the capabilities, limitations, and training methods of frontier AI systems .

Posted 9/30/2026contractRemote • United States, Bangladesh, Brazil, Colombia, Egypt, GhanaLeadWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Expertise in Machine Learning and Artificial Intelligence, with a strong focus on experimental design, data quality, and model evaluation. Proven ability to collaborate across teams and communicate complex technical findings effectively.

Highest-signal resume keywords

Ph.D. Or Master’s Degree In Artificial Intelligence7+ Years Of Professional Experience In Machine LearningStrong Python Programming SkillsResearch Experience In Synthetic Data GenerationExperience With Modern AI/ML Frameworks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningExperimental DesignModel EvaluationData QualityReproducibilityReinforcement LearningAI EvaluationAI BenchmarksData GenerationTechnical Strategy

Soft Skills

Technical CommunicationCollaborationMentoringIndependent WorkScientific Judgment

Tools & Technologies

AI/ML FrameworksResearch WorkflowsPrototyping ToolsEvaluation FrameworksData Analysis Tools

Industry Keywords

Frontier AI SystemsSynthetic DataAgentic DataModel UnderstandingTechnical ReportsOpen-Source Projects

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Investigate the capabilities, limitations, and training methods of frontier AI systems
Formulate research questions that inform AI products, platforms, and technical strategy
Explore synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation
Stay current with advances in machine learning and identify opportunities for technical contributions
Develop research-grade datasets, experiments, prototypes, tooling, and evaluation frameworks
Train, test, and evaluate models using modern AI and machine learning tools
Analyze experimental results and develop evidence-based conclusions
Establish practices for data quality, reproducibility, experimental design, and evaluation
Iterate from research hypotheses to validated technical insights
Collaborate with Research, Engineering, Product, and Operations teams to translate findings into practical applications
Communicate technical findings to specialized and cross-functional audiences
Contribute to technical reports, publications, open-source projects, workshops, or conferences where appropriate
Mentor engineers and researchers and contribute to technical discussions and peer review

Requirements

What you’ll need

Ph.D. or Master’s degree in Artificial Intelligence, Machine Learning, Computer Science, or a closely related technical field
7+ years of professional experience, including significant research engineering experience in machine learning or frontier AI systems
Strong foundations in machine learning and hands-on experience designing experiments, training models, evaluating models, or developing AI systems
Demonstrated research experience in synthetic or agentic data generation, reinforcement learning or post-training, model understanding, AI evaluation, AI benchmarks, or AI agents/tool-using systems
Strong Python programming skills with the ability to implement, test, and iterate quickly in research environments
Experience with modern AI/ML frameworks, tooling, and research workflows
Strong scientific judgment around experimental rigor, data quality, reproducibility, and evidence-based decision-making
Excellent technical communication skills and ability to work independently across research and engineering teams