Senior Applied AI Engineer

Presti and Generative AI

Presti is a fast-growing AI company incubated at Station F in Paris. We believe in the power of AI and its potential to revolutionize the world of digital imagery.

At Presti, we leverage cutting-edge technology to generate realistic, high-quality product visuals. Our innovative solution empowers brands and retailers to generate infinite product images.

The founding team developed a strong expertise in AI within leading Tech companies (Uber, BCG Gamma, Scibids) and is building a proprietary tech. As a growing entity in the tech world, we value innovation, collaboration, and a keen willingness to learn and adapt.

We are seeking an Applied AI Engineer to join our team to help us meet our customer’s needs by deriving new models from our core diffusion models.

The Role

We are looking for Applied AI Engineers who are passionate about Image-generative models and creative applications of AI.

We are seeking to grow our Applied AI team, which focuses on developing innovative models derived from our foundational diffusion models to address our client's specific requirements.

This role encompasses a research dimension while primarily focusing on the swift delivery of value to our customers.

The ideal candidate will adeptly navigate the dual priorities of leading-edge R&D initiatives and the swift deployment and iterative improvement of models.

Responsibilities

  1. Model Development: This role involves crafting specialized models, some of which are extensions of our foundational diffusion models, tailored to meet precise user requirements.
  2. Research Collaboration: Collaborate intimately with the product team to seamlessly embed these models within our products, enhancing functionality and user experience.
  3. Product development: Work closely with the product team to integrate the models into the product.
  4. Training optimization / Tooling / Dataset Engineering : Assist on different subjects closely linked to the R&D such as ****optimization of model training, model tuning, dataset engineering and tooling.

Requirements