Staff Research Engineer - AI & Machine Learning

Gramian Consulting Group

17h

About The Position

Gramian Consultancy is seeking a Staff Research Engineer to advance research and practical innovation in frontier AI systems. This role involves investigating high-impact questions, designing experiments, building prototypes, and collaborating across various teams. The focus areas include synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and AI evaluation. The engineer will translate research ideas into scalable applications and improvements for AI products and systems.

Requirements

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 at least one of the following: Synthetic or agentic data generation, Reinforcement learning or post-training, Model understanding, AI evaluation, AI benchmarks, AI agents or 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.

Responsibilities

Investigate the capabilities, limitations, and training methods of frontier AI systems.
Formulate research questions that inform AI products, platforms, and technical strategy.
Explore new approaches to 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 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 clear, evidence-based conclusions.
Establish rigorous practices for data quality, reproducibility, experimental design, and evaluation.
Iterate rapidly 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 both 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.