Staff Research Engineer - AI & Machine Learning
New
G
Gramian ConsultancyAI and machine learning
United States. Bangladesh. Brazil. Colombia. Egypt. GhanaContractStaff
Salary not disclosed
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Job Details
- Experience
- 7+ years of professional experience
- Required Skills
- PythonMachine Learning
Requirements
- Hold a Ph.D. or Master’s degree in Artificial Intelligence, Machine Learning, Computer Science, or a closely related technical field.
- Have 7+ years of professional experience, including significant research engineering experience in machine learning or frontier AI systems.
- Demonstrate strong foundations in machine learning and hands-on experience designing experiments, training models, evaluating models, or developing AI systems.
- Have research experience in at least one of synthetic or agentic data generation, reinforcement learning or post-training, model understanding, AI evaluation, AI benchmarks, or AI agents and tool-using systems.
- Have strong Python programming skills and the ability to implement, test, and iterate quickly in research environments.
- Have experience with modern AI/ML frameworks, tooling, and research workflows.
- Demonstrate scientific judgment around experimental rigor, data quality, reproducibility, and evidence-based decision-making.
- Be able 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 approaches to synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation.
- 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 evidence-based conclusions.
- Establish rigorous practices for data quality, reproducibility, experimental design, and evaluation.
- Collaborate with Research, Engineering, Product, and Operations teams to translate findings into practical applications.
- Communicate technical findings and contribute to technical reports, publications, open-source projects, workshops, or conferences.
- Mentor engineers and researchers and contribute to technical discussions and peer review.
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