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Careers.

PRESAGE is building Digital World Models (DWM) for cloud and AI infrastructure.

Modern computing infrastructure has become a vast dynamic system: compute, networks, storage, databases, accelerators, workloads, services and autonomous agents continuously interact and change one another. No human can fully reason about these systems at their current scale and speed.

We are building foundation world models that learn how digital infrastructure behaves and evolves — models that can represent its state, predict what happens next, and predict the consequences of actions before they are taken.

Our ambition is to build the intelligence layer for autonomous computing infrastructure.

Open roles — 2

Research · Paris, France · Full-time

AI Research Scientist — Digital World Models

About the role

We are looking for exceptional AI Research Scientists to advance the state of the art in Digital World Models.

You will work on fundamental research problems at the intersection of world models, representation learning, self-supervised learning, sequential prediction and large-scale machine learning.

Unlike models of the physical world, Digital World Models must learn from heterogeneous, highly structured and partially observed systems composed of resources, events, metrics, dependencies and actions. This creates a new class of research problems around representation, dynamics, causality and intervention.

You will help define those problems — and build the models that solve them.

What you will do

  • Conduct original research on Digital World Models, developing new architectures for state representation, temporal modeling, latent dynamics and action-conditioned prediction.
  • Develop models that capture interactions between resources, workloads, events, dependencies and autonomous agents across multiple temporal and structural scales.
  • Study prediction under intervention and counterfactual futures — moving from what happens next? to what happens if we do X?
  • Work closely with Research Engineers to scale datasets, training and evaluation, turning promising ideas into large-scale experiments.
  • Help define PRESAGE's scientific roadmap and contribute to significant publications and research releases.

Who you are

  • You hold a PhD in Computer Science, Artificial Intelligence, Machine Learning or a closely related field.
  • You have a strong research track record in machine learning and deep expertise in one or more areas relevant to world modeling, such as representation learning, generative or predictive modeling, sequential modeling, reinforcement learning, multimodal learning or foundation models.
  • You can turn open-ended scientific questions into clear hypotheses, experiments and measurable results.
  • You have strong practical deep-learning experience and can independently implement and evaluate new ideas using PyTorch.
  • You care deeply about experimental rigor, reproducibility and understanding why a model works — not only whether a metric improves.
  • You are excited by research environments where important questions and architectures are still being invented.

Strong additions

  • Publications at leading ML or AI conferences.
  • Experience training large models or working with distributed training infrastructure.
  • Experience with latent world models, predictive representation learning or model-based agents.
  • Experience modeling structured, relational, temporal or event-based data.
  • Familiarity with structured temporal data, distributed systems or cloud infrastructure.
  • Contributions to widely used open-source research projects or significant model releases.

Engineer · Paris, France · Full-time

AI Research Engineer — Digital World Models

About the role

We are looking for exceptional AI Research Engineers to build and scale the data, training and experimentation systems behind our Digital World Models.

You will work directly between research and engineering: building high-quality datasets, scaling model training, accelerating experimentation and implementing new architectures alongside Research Scientists.

This is not a conventional ML infrastructure role. You will understand the models deeply, contribute to experiments and help turn ambitious research ideas into working systems.

What you will do

  • Build and scale the data, training and evaluation infrastructure for Digital World Models.
  • Develop pipelines for large-scale cloud data, including resource states, telemetry, events, traces and actions, with a strong focus on dataset quality, curation, provenance and reproducibility.
  • Implement new model architectures and research ideas in close collaboration with Research Scientists, and scale them from prototypes to distributed training runs.
  • Optimize model training, data loading and inference across GPUs and large computing environments.
  • Build robust experimental tooling, benchmarks and evaluation pipelines that allow researchers to iterate quickly and compare models reliably.
  • Contribute directly to research in areas such as architecture, data, training methodology and evaluation.

Who you are

  • You have a strong background in both machine learning and software engineering.
  • You hold a Bachelor's, Master's or PhD in Computer Science, Machine Learning or a related field. A PhD is preferred.
  • You are an excellent programmer and have strong hands-on experience with Python and modern deep-learning frameworks such as PyTorch.
  • You have experience working with large datasets, GPU training and the full ML experimentation lifecycle.
  • You enjoy operating at the boundary between rapidly evolving research prototypes and reliable, scalable systems.
  • You care about performance, code quality, reproducibility and making researchers dramatically more effective.

Strong additions

  • Experience with cloud infrastructure or distributed systems.
  • Experience building large-scale dataset construction, and synthetic data generation.
  • Familiarity with time-series, event streams, graphs or other structured temporal data.
  • Experience implementing recent research papers and reproducing state-of-the-art results.
  • Publications or participation in research projects at leading AI laboratories.
  • Contributions to open-source ML frameworks, models or infrastructure.

How hiring works

  1. Choose the role that best matches your background and click “Apply for this role”.
  2. Complete the application form below.

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