Saba Sturua
AI Research Engineer II at Elastic
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolSaba Sturua is an AI Research Engineer with 7 years of experience building and productionizing machine learning systems across conversational AI and embedding-driven search. Based in Berlin, she progressed from NLP roles and internships into ML research and engineering positions at Rasa and Jina AI, and now works at Elastic, blending research rigor with pragmatic API/backend development. Her open-source contributions include improving Rasa’s incremental training and stability and implementing experiment/run management for jina-ai/finetuner, reflecting strengths in model lifecycle, tooling, and integrations like Hubble. Colleagues value her for tackling type- and quality-related technical debt as much as for delivering new features, a balance that helps teams move research into reliable products.
7 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at Free University of Tbilisi
Stackoverflow
31reputation
2kreached
3answers
0questions
Github Skills (35)
algorithm10
c-language10
python10
apidoc10
testing10
data-structure10
searching10
api10
rasa10
experiment10
data-structures10
experiment-manager10
nlp10
cpp10
cprogramming-language10
Programming languages (6)
Github contributions (5)
RasaHQ/rasa
May 2021 - Feb 2022
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
Role in this project:
ML Engineer
ML EngineerContributions:64 reviews, 137 commits, 28 PRs in 8 months
Contributions summary:Saba primarily contributed to the Rasa framework by addressing code quality issues, fixing type errors, and resolving deprecation warnings. Their work involved modifying and updating core components, including classifiers and policies, to ensure compatibility and maintainability. They also focused on implementing and refining functionality related to incremental training, specifically regarding dynamic sparse feature allocation and model checkpointing. The user's contributions are centered on improving the performance and stability of Rasa's machine learning capabilities.
nlupythonbotspeech-recognitionbotkit
jina-ai/finetuner
Apr 2022 - Jul 2022
:dart: Task-oriented embedding tuning for BERT, CLIP, etc.
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:99 reviews, 26 commits, 51 PRs in 2 months
Contributions summary:Saba primarily contributed to the development of experiment endpoints and run-related functionalities within the `finetuner` project. Their work involved implementing core features for experiment and run creation, retrieval, and deletion using Python and likely a related framework for API development. The user also refactored existing code, adding features such as logging and supporting the Hubble integration. This indicates a focus on both backend API development and integrating with external services.
nlptriplet-lossfinetuningsiamese-networkjina
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