Note: The job is a remote job and is open to candidates in USA. Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. The Staff Research Engineer will advance research and practical innovation in frontier AI systems by designing experiments, building research-grade prototypes and tooling, evaluating models, and translating research findings into scalable AI applications and product improvements.
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
Skills
- 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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