Profile

Manager at NVIDIA working on Frontier Model Evaluation, with a background spanning engineering leadership, applied machine learning, and medical image analysis research. I focus on practical AI systems: evaluation, agentic workflows, developer tooling, and building LLM-based products that are reliable and useful outside benchmark slides.

Work experience

May 2025 - Present
Manager - Frontier Model Evaluation @ NVIDIA, Cracow, Małopolskie, Poland

  • Leading work on frontier model evaluation and practical LLM evaluation systems.
  • Working across agent workflows, benchmarking, developer tooling, and evaluation infrastructure.
  • Supporting scalable evaluation work connected to NVIDIA AI products and platforms.

Sep 2024 - Apr 2025
Manager - Deep Learning and HPC Performance @ NVIDIA, Cracow, Małopolskie, Poland

  • Worked on engineering and performance-focused initiatives at the intersection of deep learning and HPC.

Nov 2022 - Aug 2024
Manager, Machine Learning @ Qualtrics, Cracow, Małopolskie, Poland

  • Led machine learning teams and initiatives focused on scaling ML capabilities within the company.

Apr 2022 - Nov 2022
Associate Manager, Machine Learning @ Qualtrics, Cracow, Małopolskie, Poland

  • Managed the ML Accelerator team responsible for scaling ML efforts at Qualtrics.

Mar 2020 - Apr 2022
Senior Applied Scientist @ Qualtrics, Cracow, Małopolskie, Poland

  • Time series forecasting and anomaly detection.
  • Automatic insight generation.
  • Data mining.

Jan 2014 - Feb 2022
Research Scientist @ Fraunhofer MEVIS, Bremen, Germany

  • Medical image analysis with deep learning and machine learning techniques.
    • Automatic organ segmentation in CT and MR.
    • Automatic liver lesion detection and segmentation in CT and MR.
    • Automatic vessel segmentation in contrast-enhanced CT and MR.
    • Real-time catheter segmentation in fluoroscopy images.
    • Explainability and uncertainty estimation of CNN models.
    • Automatic anomaly detection.
    • Statistical shape models.
  • Standalone and web application development.

Sep 2013 - Dec 2013
Junior Software Engineer @ Samsung Electronics, Łódź, Poland

  • Mobile product development.
  • Responsible for firmware-over-the-air updates and the home screen.

Jul 2012 - Sep 2012
Summer Placement @ Fraunhofer MEVIS, Bremen, Germany

  • Software development with MeVisLab within the MAVO FUS project regarding focused ultrasound therapy.

Aug 2011
Placement at Biocybernetics Department @ Foundation for Cardiac Surgery Development, Zabrze, Poland

  • Software development with LabVIEW for MAXON EC motor control.

Aug 2010
Internship @ Regional Hospital in Racibórz, Racibórz, Poland

  • Medical equipment repair and maintenance.

Education

Mar 2025 - Mar 2026
Postgraduate Degree, Project Management @ Kozminski University

2017 - Aug 2022
Doctor of Philosophy (PhD) @ Radboud University Nijmegen

  • Deep Learning-Based Segmentation in Multimodal Abdominal Imaging.

2013
Visiting Student @ ETH Zurich

2012 - 2013
Master of Science (MSc), Electronics and Telecommunications @ Wrocław University of Technology

2008 - 2012
Bachelor of Science (BSc), Bioengineering and Biomedical Engineering @ Wrocław University of Technology

Selected achievements

  • 3rd place in the MICCAI 2017 round of the international Liver Tumor Segmentation Challenge.
  • 13th place in the Global S-TopCoder 2013 November Competition.
  • Best thesis award of the Faculty of Electronics.
  • Award in the TOP10 contest for best graduates of the Faculty of Electronics.

Publications

Selected publications include work on medical image analysis, liver and tumor segmentation, explainability, and uncertainty estimation. See the publications page for a fuller list.

Languages

Polish (native) • English (full professional) • German (full professional) • Russian (elementary)

Certifications

ISTQB Certified Tester, Foundation Level

Skills

AI, ML, and Engineering

  • LLM evaluation, agent systems, benchmarking, practical AI engineering.
  • Python, C++, C, JavaScript, shell scripting, R, MATLAB, LaTeX.
  • Deep learning and scientific computing: TensorFlow, Keras, scikit-learn, scikit-image, NumPy, SciPy, pandas, OpenCV.
  • Software and tooling: Docker, git, JIRA, Confluence, web applications.