Mid / Mid+ Data Scientist (LLMs & NLP) (Remote)

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., * 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.

• 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.