Junior AI Research Engineer
Pubblicato il 06-09-2026 - Igenius in Milano
Work somewhere with the creativity of a scaleup and expertise of an enterprise. Join our AI Research Team in Milan to build the next generation of large language models, across the full life cycle: pretraining on trillions of tokens, state-of-the-art post-training, and releases spanning from a few billion to hundreds of billions of parameters. You’ll collaborate directly with leading industry labs like NVIDIA, and your work will ship into mission-critical deployments with some of the largest players in banking, defense, manufacturing, and the public sector. You’ll have first-class access to our proprietary sovereign HPC, powered by NVIDIA Blackwell GPUs and designed for frontier-scale model training, alongside additional allocations on European HPC infrastructure. As a Junior AI Research Engineer, you’ll sit at the intersection of research and production, designing and optimizing the infrastructure that trains and serves these models. Expect to touch every layer of the stack, and contribute to open-source projects shaping the European AI ecosystem. What You Have
- Master’s Degree in Computer Science, Artificial Intelligence or a related field.
- Up to 2 years of proven experience as an AI research engineer or in similar roles.
- Hands-on experience with at least one modern machine learning framework (e.g., PyTorch, TensorFlow, or JAX) through academic, research, or personal projects. Familiarity with distributed training concepts is a plus.
- Basic understanding of parallel computing concepts and high-performance systems. Exposure to CUDA programming, or compiler optimization is a plus.
- Hands-on experience with ML model debugging tools such as TensorBoard and Weights & Biases. Exposure to performance profiling tools (e.g., NVIDIA Nsight) is a plus.
- Proficiency in Python and familiarity with C++ or Rust. Exposure to AI inference, model optimization, or AI accelerators is a plus.
- Solid understanding of mathematics behind deep learning (linear algebra, probabilit