Staff Research Engineer – AI & Machine Learning

Gramian Consulting

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

Posted 9/30/2026contractRemote • India, Pakistan, Indonesia, Kenya, Nigeria, TurkeyLeadWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in Artificial Intelligence and Machine Learning, with a strong focus on experimental design, model training, and evaluation. Proven ability to develop research-grade datasets and communicate technical findings effectively across teams.

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 TrainingModel EvaluationData QualityReproducibilityEvidence-Based Decision-MakingReinforcement LearningAI EvaluationAI Benchmarks

Soft Skills

Technical CommunicationCollaborationMentoring

Tools & Technologies

AI/ML FrameworksResearch WorkflowsPrototyping ToolsEvaluation Frameworks

Industry Keywords

Frontier AI SystemsSynthetic DataAgentic Data GenerationTechnical StrategyResearch Engineering

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 informing AI products, platforms, and technical strategy.
Explore synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation.
Stay current with machine learning advances and identify meaningful 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 rigorous 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.