CAREERS AT XPDEEP
Build what machines decide with.
Machines Will Decide. We Decide How. Xpdeep builds control infrastructure for bounded AI authority: natively explainable deep learning models for intelligent machines and mission-critical industrial systems. Their learned structure can be examined, their decisions bounded, and their models evolved under control.
01 THE TECHNICAL CHALLENGE
Make a learned model carry decisions within conditions engineers define
An AI model can be accurate and still hard to rely on when its learned drivers and domain remain hidden. Xpdeep’s foundation is native structural explainability: engineers read the learned structure from within the model. The three loops are its first product architecture, supporting model design, diagnosis, frugality and bounded authority. The customer's system keeps its own rules and authority.
LOOP 01
DESIGN
Read and improve the learned model's structure through targeted local editing in Native mode, while keeping unaffected regions frozen.
LOOP 02
PRODUCTION
Make predictions structurally understandable, characterize the learned domain and, where relevant, compute constrained interventions that the customer's system can evaluate.
LOOP 03
CONTROLLED EVOLUTION
Diagnose operational feedback, make targeted corrections and validate a new model version before redeployment between operating periods.
02 THE TEAM AND ITS ORIGIN
From Grenoble research to industrial models
Xpdeep is a research spin-out from the Laboratoire d'Informatique de Grenoble, CNRS and Université Grenoble Alpes. More than two decades of research on temporal data led to the model architecture the team now develops with industrial programmes.
AHLAME DOUZAL · CO-FOUNDER & CSO
Associate professor at Université Grenoble Alpes for 25 years. 60+ publications, 10 PhD theses supervised.
STANISLAS CHESNAIS · CO-FOUNDER & CEO
Built Netsize from zero to €100M net revenue across 23 countries before its sale to Gemalto.
03 THE WORK
Where the work happens
The technology spans these areas. What each role covers is set out in its own listing below.
01
MODEL RESEARCH
Deep-learning architectures whose learned structure can be read and edited.
02
TEMPORAL DATA AND SIGNALS
Multivariate time series and signals, where Xpdeep's edge is deepest.
03
MODEL ENGINEERING
PyTorch-compatible models that train, run and deploy within real constraints.
04
VISUALIZATION
Making learned structure, domain state and interventions legible to engineers.
05
INDUSTRIAL INTEGRATION
Fitting model outputs into the customer's own systems, rules and validation chain.
04 OPEN POSITIONS
Open positions
Roles, locations and contract details come directly from our recruitment platform, in the language each listing is written in. To apply, open a listing and use its application button.