Independently own and execute specific research tasks or sub-projects.
Design and implement complex experiments to test new hypotheses.
Analyze and interpret results to draw clear conclusions.
Contribute high-quality, reusable code to the team's repository.
Actively contribute to improving the internal benchmark by identifying data gaps and proposing new evaluation metrics.

PhD or Master's degree in Computer Science, Computational Linguistics, Machine Learning, or a related quantitative field.
3+ years of hands-on experience in applied NLP research or ML engineering.
Ability to operationalize subjective concepts into concrete guidelines.
Proven track record of translating complex technical requirements for non-technical stakeholders.
Deep theoretical and practical understanding of Transformer architectures, Attention mechanisms, Positional Embeddings, and Tokenization strategies.
Extensive experience with the post-training stack: Supervised Fine-Tuning and Preference Alignment techniques.
Experience with noisy label handling and identifying semantic bias in large-scale datasets.
Understanding Function/Tool Calling, ReAct frameworks, and evaluating "trajectory" quality.
Experience designing LLM-as-a-Judge pipelines and reference-free metrics.

AI Research Engineer

Key skills