Neuralk AI

At Neuralk-AI, we develop our own AI embedding models specialized for structured data representation, allowing enterprises to build custom AI solutions that accurately interact with their own structured data. Our models leverage Graph Neural Networks (GNNs)-based models to deliver exceptional performance in representation and transfer learning tasks on structured datasets, giving these models a great potential for data cleaning and enrichment solutions.

neuralk-ai.com

Neuralk AI

neuralk-ai.com

HQFR

Team Size3

Open Jobs4

Total Funding-

Latest FundraiseUnknown

TL;DR

What they do: Build tabular/tabular‑foundation models and APIs for structured (tabular) enterprise data

Headquarters: Paris, Île‑de‑France, France

Founders: Antoine Moissenot (CEO) and Alexandre Pasquiou (CSO)

Stage & funding: Pre‑seed; $4M round led by Fly Ventures (Feb 2025)

Tech focus: Graph Neural Networks (GNN)‑based embeddings for structured data

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Company Overview

Problem Domain

Representation and predictive modeling for structured (tabular) enterprise data

Founded

2023

Industry

IT Services and IT Consulting

Tech Stack

Graph Neural Networks (GNNs)

Tabular foundation/embedding models

APIs for model access

Funding Track Record

Pre‑seed- February 2025

$4 million

Round included participation from StemAI and multiple angel investors

Investor Signal

“Led by Fly Ventures with participation from StemAI and prominent angel investors (including Thomas Wolf and others)”

Founders

What we do

Join the Team

AI Scientist

On-SiteParis, FR

On-Site • Paris, FR

About the Company

Neuralk-AI is a passionate team leading the way in AI innovation, committed to driving the rapid adoption of transformative AI applications in the industry. Our focus is on developing an agent based on Tabular AI to allow any company to build AI applications that natively interact with their structured databases (tabular). Specifically, we develop a modern AI workflows platform that automatically solves use-cases with state-of-the-art performance without custom training on your data.

As an early-stage AI-driven startup backed by significant funding ($4M), our AI-agent is powered by our proprietary Tabular Foundation Model, driving practical business solutions from research. We value clear communication and simplicity in our approaches, promoting a constant optimization mindset.

Join Neuralk to be part of a growing team, eager to learn and adapt, united by the belief that our technology can make a significant positive impact and contribute to transforming the AI industry.

Co-founders: Alexandre Pasquiou (CSO) & Antoine Moissenot (CEO)
Equal Opportunity Employment: Neuralk is dedicated to equal opportunity employment and fosters an environment that is open and respectful of diversity. All applicants are encouraged to apply if you have at least 3 years of expertise, passion for our mission, learn quickly, and believe you can contribute.

About the Role

Job Title: AI Scientist
Location: Station F, Paris
Employment Type: Full Time
Department: Open

Mission Highlights

As a Machine Learning Researcher, your role will be to improve in-house AI embedding models for structured data representation and translate them into actionable insights for strategic applications, which you will develop with our industrial and academic partners. You will collaborate closely with our engineering team (~8 people) to enhance the performance, scalability, and impact of our AI-driven solutions, while also engaging with clients to answer their needs and deliver easily adaptable pre-trained/fine-tuned Tabular foundation models.

Role & Responsibilities

You will be responsible for:

Profile

Education: PhD in Computer Science, Machine Learning, or a closely related field, with a focus on deep learning.

Bonuses

Publication record in top-tier ML conferences or journals.
Experience in designing and running large-scale ML experiments (SLURM, Pytorch, Deepspeed).
Demonstrated machine learning experience in open-source activity or data science competitions.
Track record of translating research into business impact.
Experience in developing and debugging in C/C++, Python.

Expertise

Machine Learning: Deep understanding of ML theories and practices, especially related to reproducibility and scalability.
Embedding Models: Experience in designing, training, and evaluating embedding models.
Programming: Proficient in Python and AI frameworks and tools (e.g., Sklearn, PyTorch, Jax), with experience in software development best practices and version control systems such as Git.
Data Management: Familiarity with data structures and database systems (Parquet, SQL, and NoSQL) to manage and process large datasets efficiently.

Teeming tracks opportunities at over 24,000 AI startups, then works with you to find (and land) the one you'll love.

Machine Learning Engineer

Part-timeUtrecht, NL

Part-time • Utrecht, NL

Frontend Developer

Part-timeCambridge, GB

Part-time • Cambridge, GB

DevOps Engineer

Full-timeManchester, GB

Full-time • Manchester, GB

Data Scientist

Part-timeAustin, US

Part-time • Austin, US

Software Engineer

InternshipTel Aviv

Internship • Tel Aviv

Technical Writer

ContractManchester, GB

Contract • Manchester, GB

Algorithms: Contribute to the development of machine learning models for structured data representation in collaboration with dedicated teams.

Evaluation: Continuously evaluate and optimize the performance of our ML models by building adapted metrics reflecting the use-cases of our clients, building upon the insights from our industrial and academic partners.

Active Learning and Training Data Optimization: Participate in the active learning strategy and implementation process to improve sample selection and future model performance. Design and consolidate training and evaluation datasets to optimize representational as well as transfer learning abilities of our Tabular Foundation models.

Research: Stay current with the latest ML advancements in the field and suggest optimizations that may improve the foundation models’ performance and capabilities.

Pitching & Communication: Present both ML research concepts to the scientific community and experimental design needs to the ML team.

Collaboration: Work closely with ML engineers, data scientists, and clients to deliver promising representation algorithms for downstream applications.

Ad-hoc Analyses: Run analyses to understand the learning mechanisms of the foundation model.

Experience: 3+ years in machine learning and software engineering-related positions involving training, fine-tuning, and evaluating DL algorithms (Transformers) in the cloud or in a private cluster.

Skills:

Strong experience in machine learning, particularly training ML models and designing new learning paradigms.
Excellent communication skills in English.
Proven ability to work with interdisciplinary teams.
Thrives in a fast-paced, evolving startup environment.
Self-starter and autonomous.
Strong analytical skills and problem-solving ability.
Appetite to explore, implement new ideas, and innovate.

AI Platforms: Experience with deploying and managing machine learning models, including familiarity with Pytorch, SLURM, and Deepspeed (or similar).