[Hiring] Staff Research Engineer @Gramian Consulting Group

Role Description

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
• AI evaluation

You will help translate promising research ideas into scalable applications and improvements to AI products and systems.

Qualifications

• 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.

Requirements

• 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.

Benefits