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# AI/ML Scientist

- **Company:** Arago
- **Location:** Courbevoie, France
- **Contract:** Permanent contract
- **Skills:** Python (Programming Language), Pytorch, Large Language Models, Information Technology
- **Published:** September 5, 2026
- **Apply:** https://startup.jobs/ai-ml-scientist-quantization-numerical-robustness-arago-8692609

## About the Role

* Strong background in mathematics, physics, computer science, or a related quantitative field, with solid foundations in numerical methods, probability, and statistics.
 * Deep experience with ML quantization techniques, including PTQ, QAT, quantization-aware fine-tuning, mixed precision, and low-bit weight/activation formats.
 * Experience studying the impact of numerical precision, approximation, perturbations, or hardware noise on model accuracy and stability.
 * Strong understanding of modern model architectures, including LLMs, diffusion models, multimodal/video models, and/or world models.
 * Ability to design rigorous, large-scale experiments and analyze accuracy/robustness trade-offs across models, layers, operators, and numerical formats.
 * Good understanding of accelerator architecture, inference performance, memory/computation trade-offs, and the interaction between model-level techniques and hardware efficiency.
 * Strong Python/PyTorch skills; experience with custom operators, simulators, emerging accelerator stacks, or research prototypes is a strong plus.
 * Language: English at a proficient level. French is a plus.

## Description

* Design and run large-scale experiments to characterize model sensitivity to analog noise, reduced precision, and other non-idealities of Arago's accelerator.
 * Develop and validate numerical and noise models representative of hardware behavior.
 * Research and implement quantization approaches spanning training, fine-tuning, post-training, and runtime/on-the-fly techniques.
 * Identify model-, layer-, and operator-level precision requirements and provide recommendations to Arago's hardware and software teams.
 * Optimize the trade-off between model quality, numerical robustness, and inference performance.
 * Work closely with hardware, compiler, runtime, and inference teams to translate research findings into capabilities of Arago's evolving software stack.


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