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Ai research engineer (sant'ambrogio di torino)

Sant'Ambrogio di Torino
Domyn
Pubblicato il Pubblicato 9h fa
Mansioni della posizione
Work somewhere with the creativity of a scaleup and expertise of an enterprise.

Competenze, Esperienza, Qualifiche: se possiede i requisiti giusti per questa possibilità, si candidi oggi stesso.

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 an 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

PhD in Computer Science, Artificial Intelligence or a related field, or equivalent practical experience.
At least 5 years of proven experience as an AI research engineer or more than 2 years of experience and a PhD
Expertise in modern machine learning frameworks such as PyTorch, TensorFlow, and JAX, with deep knowledge of distributed training (Pytorch Distributed, Ray, DeepSpeed).
Strong background in parallel computing and high-performance systems, including CUDA programming and compiler optimizations.
Hands-on experience with ML model debugging and performance profiling tools (TensorBoard, Weights & Biases, NVIDIA Nsight).
Proficiency in Python and C++ or Rust

,

particularly for high-performance inference and AI accelerators.
Solid understanding of mathematics behind deep learning (linear a
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