About the job offer

Neuralk-AI is looking for an AI Scientist with experience in AI model design and training.

You must have a PhD to apply.

You must have validate one of the following option below:

• you have pre-trained large transformer-based models -> pre-training team
• you have fine-tuned large transformer-based models -> fine-tuning team
• you have worked on model training and data distribution modelling -> synthetic data modelling team

You will report to the CSO of Neuralk and will be located in our Paris offices.

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About Neuralk

We are 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 a 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 solve your 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).

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, we want to hear from you.

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.

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Role & Responsibilities:

By contributing to the core of our AI workflows platform, this position directly supports the company’s mission of making AI accessible and useful. You will be responsible for:

• 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 optimisation: Participate in the active learning strategy and implementation process to improve sample selection and future model performance. As well as designing and consolidating training and evaluation datasets to optimise 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 optimisations 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: Running analyses to understand the learning mechanisms of the foundation model.

Profile:

• PhD in Computer Science, Machine Learning or a closely related field, with a focus on deep learning.
• 3+ years of experience in machine learning and software engineering-related positions which involved training, fine-tuning and evaluating DL algorithms (Transformers) in the cloud or in a private cluster.
• Strong experience in machine learning, in particular 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.

Bonuses:

• You have a publication record in top-tier ML conferences or journals
• You have demonstrated experience in designing and running large-scale ML experiments (SLURM, Pytorch, Deepspeed).
• Demonstrated machine learning experience in one of the following: open-source activity, 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.‍
• AI platforms: Experience with deploying and managing machine learning models, including familiarity with Pytorch, SLURM and Deepspeed (or similar).

Compensation & Benefits:

We are a fast-pace startup, yet, we favor a good work-life balance and interesting compensations. We offer:

• A competitive salary
• Equity (BSPCE), to reflect the value you bring to Neuralk and to foster a shared journey
• Comprehensive health insurance 
• French level paid leave and time-off work
• Dynamic work setting. Although our preference is for in-person collaboration, we will be flexible with occasional remote work arrangements.
• and more to come as we grow