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Gramian Consulting Group

Staff Research Engineer - AI & Machine Learning

Posted Yesterday
Remote
Hiring Remotely in United States
Senior level
Remote
Hiring Remotely in United States
Senior level
Conduct research on frontier AI systems, including synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarking, and evaluation. Design rigorous experiments, datasets, prototypes, tooling, and research workflows; train and assess models; analyze results; and translate findings into scalable AI products. Collaborate across research, engineering, product, and operations teams, communicate technical conclusions, contribute to publications or open-source work, and mentor engineers and researchers.
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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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