Conduct frontier AI research across synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. Design experiments, datasets, prototypes, tooling, and research frameworks; train and assess models; analyze results; ensure rigor and reproducibility; communicate findings; collaborate across teams; contribute to publications and open-source work; and mentor engineers and researchers.
About Gramian
Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.
About the Role
We are looking for a Staff Research Engineer to advance research and practical innovation in frontier AI systems. You will investigate high-impact questions, design rigorous experiments, build research-grade prototypes and tooling, and collaborate across Research, Engineering, Product, and Operations teams.
The role focuses on areas including synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and AI evaluation. You will help translate promising research ideas into scalable applications and improvements to AI products and systems.
SENIORITY: Staff level — 7+ years
Key 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.
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.
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