Applied Ai Researcher - Foundation Models
Pubblicato il 18-09-2026 - Translated in Lazio
Overview
As a Deep Learning Scientist in the Foundation Models team, you will drive open multilingual LLM pre-training research from data curation to model evaluation. You'll design scalable experiments, explore architectures and optimization strategies, and develop multilingual training methods to advance open models beyond English. You'll work closely with researchers and engineers to translate hypotheses into large-scale experiments on GPU/HPC infrastructure. This role offers the chance to shape Translated's next generation of multilingual language models and contribute to state-of-the-art benchmarks.
Retribuzione / Benefits
flexible smart-working policy
Rome headquarters with on-site facilities
health-oriented initiatives (massages, sauna, gym, swimming pool)
psychological, legal, and financial support
growth-oriented learning paths and networking events
fully equipped kitchens and cafeteria
Responsabilità
design and conduct LLM pre-training research
plan and run scalable experiments, implement and analyze results
investigate architectures, optimization, training dynamics, and scaling behavior
develop multilingual training strategies
conduct data selection, curation,
and mixture design for pre-training
evaluate models on multilingual and general benchmarks
monitor state-of-the-art LLMs and benchmark progress
execute experiments on large-scale GPU and HPC infrastructure
translate research hypotheses into measurable experiments for large-scale training
Requisiti fondamentali
3+ years of relevant experience in Deep Learning, ML, NLP, or LLMs
strong understanding of modern deep learning and transformer models
excellent Python programming
experience designing and running ML experiments
familiarity with GPU-based training and Unix/Linux systems
ability to analyze results and make research decisions empirically
interest in large-scale experimental research and language model pre-training
ability to read and reproduce recent scientific literature
excellent written and spoken English
ability to collaborate effectively with researchers and engineers
collaboration
analytical thinking
clear communication
transformer-based language models
Python programming
GPU-based training