Conduct applied research related to AI model evaluation, data quality, and validation methodologies
Co-author research papers, technical reports, and whitepapers
Implement research prototypes and experimental systems
Support internal R&D initiatives in areas such as Agentic AI systems, LLM evaluation and validation, Speech and multimodal model assessment, Data-centric AI methodologies
Collaborate with the engineering team to translate research ideas into practical workflows
Develop experimental code and proof-of-concept implementations
Work with datasets used for training, evaluation, and benchmarking
Design experiments and analyze results
Write technical blog articles explaining recent advances in AI and ML
Translate complex research topics into accessible technical content
Support marketing and communications teams with technically accurate material
Contribute educational materials and technical explainers
Attend academic and industry conferences
Engage with researchers from AI companies and academia
Discuss research topics, evaluation challenges, and data requirements
Identify opportunities for collaboration related to dataset needs and model evaluation
Maintain professional follow-up communication after conferences

Current PhD student, doctoral candidate, or recent graduate in: Machine Learning / Artificial Intelligence / Natural Language Processing / Speech Processing / Computer Science or related field
Strong understanding of modern AI models (LLMs, speech models, or multimodal systems)
Experience implementing research code in Python
Familiarity with common ML frameworks
Ability to read and understand academic papers
Strong written English skills
Interest in applied research and real-world deployment challenges

Summa Linguae

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