Ural Logistics
ML Engineer (Python, PyTorch, fine-tuning)
Work format: office or hybrid (Moscow)
☑️ Responsibilities
- Build data preparation pipelines: collection, cleaning, normalization, deduplication, and labeling
- Form training, validation, and independent evaluation datasets, control for data leakage and overlaps
- Create baselines for tasks of extraction, classification, entity and relation recognition
- Fine-tune models, prepare instruction sets, train adapters, and compare results with baseline models
- Define metrics, stratify datasets, and analyze errors by document types, scenarios, and fields
- Work with entity and relation extraction for building and updating a graph representation of data
- Deploy local inference and optimize it for available infrastructure
- Work with tensor parallelism, batching, context length, GPU memory, and quantization
☑️ Our requirements for candidates
- Tech Stack: Python, PyTorch, Hugging Face Transformers, PEFT, pandas, SQL, PostgreSQL, Airflow (or equivalent), MLflow (or equivalent), vLLM, CUDA, Docker, Kubernetes
Contacts: kudryashova.e@ulgroup.ru
