Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with the uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.
About the client and the role: Our client is a software company that develops and distributes software for macOS and iOS. We are seeking a Senior ML Engineer, wo will fine-tune and optimize the latest LLMs using cutting-edge techniques (LoRA, Q-LoRA, DPO, GRPO), build scalable ML pipelines, and shape how AI seamlessly integrates into everyday Mac experiences.
Responsibilities: — Train and fine-tune LLMs (e.g., LLaMA, Gemma, Qwen) for specialized tasks (such as function calling, charting, parameter parsing) and ensure their effective deployment across diverse environments, including on-device (MLX) — Develop and apply model optimization and compression techniques (e.g., dynamic pruning, quantization) to enable high-performance on-device inference — Conduct rigorous evaluation and benchmarking of LLMs to measure performance, accuracy, and efficiency across different setups — Adapt LLMs for domain-specific tasks using techniques such as prompt engineering, adapter-based fine-tuning (PEFT), and multi-modal extensions.
Requirements: — Strong background in classical ML and NLP — Solid knowledge of deep learning, going beyond just LLMs (e.g., CNNs, RNNs, Transformers, Autoencoders) — Proficiency in Python and deep learning frameworks (e.g., PyTorch) — Deep experience with LLMs: training large and small models using various techniques (fine-tuning, post-fine-tuning, DPO, GRPO) — Practical experience with LoRA / Q-LoRA and other adapter-based fine-tuning methods — Hands-on experience in model optimization (pruning, quantization), evaluation, and deployment — Ability to design and automate ML pipelines with a focus on scalability and maintainability — Upper-intermediate English & fluent Ukrainian
Would be a plus: — Experience working with multi-modal data: structured/tabular, images, audio, or video — Experience in synthetic data generation — Hands-on experience with LLM production deployment: monitoring, evaluation, and metrics tracking — Familiarity with on-device ML and performance optimization — Swift knowledge or interest, as the company’s products operate in the Apple ecosystem — Background in computer vision, time series, or reinforcement learning
The company offers: — Hybrid work model — Health and mental programs — Flexible working hours — Time-off policy — Team and growth — Different social initiatives
