We are looking for an experienced Data Scientist specializing in Large Language Models (LLMs) to join our fast-growing team. You will work with state-of-the-art models such as GPT and Claude, driving applied research and real-world product delivery. This role is ideal for someone passionate about NLP, deep learning, and pushing the limits of generative AI.
Details
* Location: Remote (Ukraine or EU-friendly time zones)
* Employment Type: Full-time, Contract
* Start Date: ASAP
* Language Requirements: Ukrainian & English (fluent)
Key Responsibilities
* Design, develop, and optimize LLMs for NLP use cases: text generation, summarization, translation, Q&A.
* Conduct applied research and experiments to extend LLM performance and capabilities.
* Collaborate with engineering, product, and research teams to integrate LLMs into production systems.
* Build and maintain pipelines, tools, and infrastructure for training, fine-tuning, deploying, and monitoring LLMs.
* Analyze model results, troubleshoot errors, and implement accuracy and performance improvements.
* Stay up-to-date with cutting-edge AI/ML breakthroughs and evaluate their relevance for the product.
* Explain complex concepts clearly to both technical and non-technical stakeholders.
Requirements
* MS or PhD in Computer Science, Data Science, AI, Mathematics, or related field.
* 4+ years of hands-on experience with deep learning, particularly NLP and transformers.
* Strong expertise in Python, PyTorch, TensorFlow, and modern deep learning frameworks.
* Deep understanding of LLM architectures, attention mechanisms, transformers, and seq-to-seq models.
* Experience training, fine-tuning, scaling, and deploying LLMs.
* Practical knowledge of model optimization, inference, and serving.
* Strong analytical mindset and problem-solving skills.
* Excellent communication abilities and a teamwork mindset.
* Fluency in Ukrainian and English.
Nice to Have
* Previous research experience in NLP, LLMs, or machine learning.
* Experience with multi-modal data (text, audio, image).
* Familiarity with AWS, GCP, or other cloud platforms for large-scale training.
* Understanding of MLOps, production ML workflows, and monitoring.
* Background in information retrieval, knowledge graphs, or reasoning models.