Michal S.

Responsibilities:

• Developed a comprehensive evaluation and benchmarking framework for computer-vision models, enabling reproducible experiments and consistent metric tracking across classification, detection, and segmentation tasks.

• Implemented a unified evaluation pipeline with modular dataset adapters, a typed AppConfig configuration system, and a standardized Runner architecture that ensures deterministic, task-aligned execution.

• Designed an ONNX-first workflow featuring automatic format detection, model conversion, and inference-parity validation, enabling consistent and optimized evaluation regardless of input model format.

• Extended the system with a flexible plugin mechanism supporting both deep-learning models and classical algorithms.

• Architected backend components using PostgreSQL, MinIO (S3), Redis, Celery, and Docker, supporting multi-client workload distribution, artifact persistence, and scalable execution.

• Engineered an incremental evaluation engine incorporating per-sample hashing, smart caching, and partial-resume capabilities, significantly reducing repeated runtime and improving overall throughput.

• Integrated the evaluation pipeline into a full PyQt desktop application, connecting model upload, benchmark selection, configuration overrides, batch-based progress reporting, live logs, and dynamic metric comparison tables.

• Research: Conducted an in-depth study of Transformer architectures, including attention mechanisms and token-processing workflows. Leveraged this research to implement an LLM-based Intent Router that maps free-text instructions into task, domain, and benchmark selections, enabling automated configuration from natural-language input.

• Delivered an extensible, scalable, and production-oriented evaluation platform with consistent metrics, artifact exporting, and cross-benchmark comparison capabilities.