Full-Time

AI/ML Scientist

Quantization & Numerical Robustness

Updated on 9/3/2026

Arago

Arago

11-50 employees

Photonic AI chips for energy efficiency

No salary listed

Île-de-France, France

In Person

Category
AI & Machine Learning (1)
Required Skills
LLM
Python
PyTorch
Machine Learning

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Requirements
  • Strong background in mathematics, physics, computer science, or a related quantitative field, with solid foundations in numerical methods, probability, and statistics.
  • Deep experience with machine learning quantization techniques, including post-training quantization, quantization-aware training, quantization-aware fine-tuning, mixed precision, and low-bit weight and 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 large language models, diffusion models, multimodal or video models, and/or world models.
  • Ability to design rigorous, large-scale experiments and analyze accuracy and robustness trade-offs across models, layers, operators, and numerical formats.
  • Good understanding of accelerator architecture, inference performance, memory and computation trade-offs, and the interaction between model-level techniques and hardware efficiency.
  • Strong Python and PyTorch skills.
  • Proficient English language ability.
Responsibilities
  • Research how reduced precision, analog noise, and other hardware non-idealities affect modern artificial intelligence models, and develop quantization and robustness techniques tailored to the custom artificial intelligence accelerator.
  • Design and run large-scale experiments to characterize model sensitivity to analog noise, reduced precision, and other non-idealities of the 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 or on-the-fly techniques.
  • Identify model-, layer-, and operator-level precision requirements and provide recommendations to 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 the evolving software stack.
Desired Qualifications
  • Experience with custom operators, simulators, emerging accelerator stacks, or research prototypes.
  • French language ability.

Arago develops energy-efficient AI hardware using photonic technology. It builds a proprietary photonic processor called JEF that processes data with light instead of electricity, aiming to cut AI compute energy use by 10x–30x while delivering comparable performance to current GPUs. The processor combines photonics, electronics, and software in a hybrid architecture, and is paired with a software stack named CARLOTA that lets AI developers run models on standard frameworks like PyTorch without changing code. This makes it easier to deploy photonic acceleration in AI workloads. Arago differentiates itself by targeting substantial energy savings at scale through photonic computing and by providing an integrated toolchain that abstracts hardware complexity for researchers and developers. Its goal is to reduce the energy consumption of AI computations in data centers and other compute-intensive environments, enabling high-performance AI with lower power use.

Company Size

11-50

Company Stage

Seed

Total Funding

$24.3M

Headquarters

Paris, France

Founded

2024

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Simplify Jobs

Simplify's Take

What believers are saying

  • Arago raised $26 million in July 2025 from Earlybird, Protagonist, and Visionaries Tomorrow.
  • April 2026 Synopsys and Cadence partnerships broaden access to critical EDA and IP.
  • Station F and GlobalFoundries publicized Arago’s first digital-optical chip, amplifying hiring and customer interest.

What critics are saying

  • GPU incumbents keep getting cheaper and faster, squeezing Arago before 2027 commercialization.
  • Photonic chips need flawless silicon-photonics integration; one yield miss can reset Arago's roadmap.
  • If JEF misses production customers after the 2026 tape-out, Arago becomes a science project.

What makes Arago unique

  • Arago’s April 2026 tape-out with GlobalFoundries validates standard-silicon photonic AI manufacturing.
  • JEF combines photons and electronics, targeting 10x-30x lower energy than GPUs.
  • CARLOTA hides photonic complexity and plugs into PyTorch without code rewrites.

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Benefits

Health Insurance

Stock Options

Relocation Assistance

Paid Vacation

Commuter Benefits

Professional Development Budget

Growth & Insights and Company News

Headcount

6 month growth

7%

1 year growth

7%

2 year growth

18%
Dataweek
Aug 27th, 2025
Arago raises $26M for photonic AI chip

Arago, a deeptech startup, has raised $26 million in seed funding to commercialize its photonic AI chip, 'JEF'. This chip uses lasers to process data with photons, offering 10x lower energy consumption than current GPUs. Arago's technology is compatible with existing AI ecosystems and aims to revolutionize AI compute infrastructure. The funding will accelerate product development, expand the team, and enhance business partnerships across France, North America, and Israel.

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EU-Startups
Jul 8th, 2025
French DeepTech company Arago raises €22.1 million to slash AI energy consumption with photonic chip | EU-Startups

Arago, a Paris-based DeepTech startup pioneering a new class of energy-efficient AI chips powered by light, has raised €22.1 million in Seed funding to

Maddyness
Jul 8th, 2025
Arago AI raises $26M for energy-efficient AI

Arago, a French startup, has raised $26 million to address the high energy consumption of AI by developing a photonic processor named "JEF." Founded in April 2024, Arago aims to reduce energy use by utilizing lasers to process data with photons, which generate less heat than traditional electrons. This technology promises to be ten times more energy-efficient than current GPUs. The funding round was led by Earlybird, Protagoniste, and Visionaries Tomorrow, with participation from notable tech figures.