Job description
We are seeking an experienced Senior/Middle Data Scientistwith a passion for Large Language Models (LLMs) and cutting-edge AI research. In this role, you will design and implement state-of-the-art evaluation and benchmarking framework to measure and guide model quality, and personally train LLMs with a strong focus on Reinforcement Learning from Human Feedback (RLHF). You will work alongside top AI researchers and engineers, ensuring our models are not only powerful but also aligned with user needs, cultural context, and ethical standards. The benchmarks and feedback loops you own serve as the contract for quality—gating releases, catching regressions before users do, and enabling compliant, trustworthy features to ship with confidence.
About us
Kyivstar.Tech is a Ukrainian hybrid IT company and a resident of Diia.City. We are a subsidiary of Kyivstar, one of Ukraine’s largest telecom operators.
Our mission is to change lives in Ukraine and around the world by creating technological solutions and products that unleash the potential of businesses and meet users’ needs.
Over 600+ KS.Tech specialists work daily in various areas: mobile and web solutions, as well as design, development, support, and technical maintenance of high-performance systems and services.
We believe in innovations that truly bring quality changes and constantly challenge conventional approaches and solutions. Each of us is an adherent of entrepreneurial culture, which allows us never to stop, to evolve, and to create something new.
Responsibilities:
• Analyze benchmarking datasets, define gaps and design, implement, and maintain comprehensive benchmarking framework for Ukrainian language.
• Research and integrate state-of-the-art evaluation metrics for factual accuracy, reasoning, language fluency, safety, and alignment.
• Design and maintain testing frameworks to detect hallucinations, biases, and other failure modes in LLM outputs.
• Develop pipelines for synthetic data generation and adversarial example creation to challenge the model’s robustness.
• Collaborate with human annotators, linguists, and domain experts to define evaluation tasks and collect high-quality feedback.
• Develop tools and processes for continuous evaluation during model pre-training, fine-tuning, and deployment.
• Research and develop best practices and novel techniques in LLM training pipelines.
• Analyze benchmarking results to identify model strengths, weaknesses, and improvement opportunities.
• Work closely with other data scientists to align training and evaluation pipelines.
• Document methodologies and share insights with internal teams.
Required Qualifications:
Education & Experience:
• 3+ years of experience in Data Science or Machine Learning, preferably with a focus on NLP.
• Proven experience in machine learning model evaluation and/or NLP benchmarking.
• Advanced degree (Master’s or PhD) in Computer Science, Computational Linguistics, •Machine Learning or a related field is highly preferred.
NLP Expertise:
• Good knowledge of natural language processing techniques and algorithms.
• Hands-on experience with modern NLP approaches including embedding models, sematic search, text classification, sequence tagging (NER), transformers/LLMs, RAGs.
• Familiarity with LLM training and fine-tuning techniques.
ML & Programming Skills:
• Proficiency in Python and common data science and NLP libraries (pandas, NumPy, scikit-learn, spaCy, NLTK, langdetect, fasttext).
• Strong experience with deep learning frameworks such as PyTorch or TensorFlow for building NLP models.
• Solid understanding of RLHF concepts and related techniques (preference modeling, reward modeling, reinforcement learning).
• Ability to write efficient, clean code and debug complex model issues.
Data & Analytics:
• Solid understanding of data analytics and statistics.
• Experience creating and managing test datasets, including annotation and labeling processes.
• Experience in experimental design, A/B testing, and statistical hypothesis testing to evaluate model performance.
• Comfortable working with large datasets, writing complex SQL queries, and using data visualization to inform decisions.
Deployment & Tools:
• Experience deploying machine learning models in production (e.g., using REST APIs or batch pipelines) and integrating with real-world applications.
• Familiarity with MLOps concepts and tools (version control for models/data, CI/CD for ML).
• Experience with cloud platforms (AWS, GCP or Azure) and big data technologies (Spark, Hadoop, Ray, Dask) for scaling data processing or model training is a plus.
Communication:
• Experience working in a collaborative, cross-functional environment.
• Strong communication skills to convey complex ML results to non-technical stakeholders and to document methodologies clearly.
Preferred Qualifications:
Advanced NLP/ML Techniques:
• Prior work on LLM safety, fairness, and bias mitigation.
• Familiarity with evaluation metrics for language models (perplexity, BLEU, ROUGE, etc.) and with techniques for model optimization (quantization, knowledge distillation) to improve efficiency.
• Knowledge of data annotation workflows and human feedback collection methods.
Research & Community:
• Publications in NLP/ML conferences or contributions to open-source NLP projects.
• Active participation in the AI community or demonstrated continuous learning (e.g., Kaggle competitions, research collaborations) indicating a passion for staying at the forefront of the field.
Domain & Language Knowledge:
• Familiarity with the Ukrainian language and context.
• Understanding of cultural and linguistic nuances that could inform model training and evaluation in a Ukrainian context.
• Knowledge of Ukrainian benchmarks, or familiarity with other evaluation datasets and leaderboards for large models can be an advantage given our project’s focus.
MLOps & Infrastructure:
• Hands-on experience with containerization (Docker) and orchestration (Kubernetes) for ML, as well as ML workflow tools (MLflow, Airflow).
• Experience in working alongside MLOps engineers to streamline the deployment and monitoring of NLP models.
Problem-Solving:
• Innovative mindset with the ability to approach open-ended AI problems creatively.
• Comfort in a fast-paced R&D environment where you can adapt to new challenges, propose solutions, and drive them to implementation.
What we offer:
• Office or remote – it’s up to you. You can work from anywhere, and we will arrange your workplace.
• Remote onboarding.
• Performance bonuses for everyone (annual or quarterly — depends on the role).
• We train employees: with the opportunity to learn through the company’s library, internal resources, and programs from partners.
• Health and life insurance.
• Wellbeing program and corporate psychologist.
• Reimbursement of expenses for Kyivstar mobile communication.
