Senior deep learning scientist- dvps
Pubblicato il 14-10-2026 - Pi School in Italia
About Pi School Pi School is on a mission to empower businesses and professionals to leverage AI's transformative capabilities. We believe technology should be an enabler, not a barrier, and it should empower individuals and businesses to grow, innovate, and stay connected. We strive for a world where AI and human potential coexist harmoniously, reshaping the way we work, learn, and prosper.
Born in 2017 from the expertise of Translated, an industry pioneer with over two decades of leadership in AI + human translation, Pi School has helped over 100 companies integrate AI solutions to improve their operations, enhance decision-making, and achieve sustainable growth.
Pi School remains at the forefront of the ever‐evolving AI landscape and collaborates with prominent research centres and organisations worldwide, such as the European Space Agency (ESA) and the European Commission, to harness AI for both cutting‐edge research and practical solutions, driving progress in science and beyond. Our approach is founded on purposeful, impactful work, leveraging technology as a force for good.
Learn more about our company: Senior Deep Learning Scientist – Rome We are looking for a Senior Deep Learning Scientist to work in our office in Rome. Pi School has just been awarded a grant on DVPS, a 29 M€, 4‐year collaborative research project starting in July 2025. DVPS develops the science of multimodal foundation models and deploys them on problems in Earth observation, language and cardiology.
The project is endowed with computing resources matching its ambition, estimated to run on 30 M GPU‐hours provided by Euro HPC on major Exascale and Petascale compute infrastructure in Europe. All research outcomes of DVPS, such as trained models, datasets, and evaluation benchmarks, will be open‐sourced on Hugging Face.
Responsibilities
Work with data, compute and algorithms Design deep learning multimodal neural architectures Design experiments, implement them in code, run them on large (GPU