Research Engineer / Scientist, Post-training - Paris

About H: H exists to push the boundaries of superintelligence with agentic AI. By automating complex, multi-step tasks typically performed by humans, AI agents will help unlock full human potential.

H is hiring the world’s best AI talent, seeking those who are dedicated as much to building safely and responsibly as to advancing disruptive agentic capabilities. We promote a mindset of openness, learning, and collaboration, where everyone has something to contribute.

About the Team: The Models team builds the foundational models that power our cutting-edge agentic technology. We focus on training techniques to optimize model capabilities specifically for agent applications. This allows us to achieve the best performance at a given inference cost. Our work spans the development of Large Language Models (LLMs) and Vision-Language Models (VLMs), enabling agents to perceive, understand, and act within complex environments. We are deeply involved in enhancing these models through training methods with a focus on improved instruction following, tool use, and interaction with dynamic environments via large-scale reinforcement learning and reward modeling. We operate at the intersection of research and product, translating cutting-edge research into practical solutions that drive the next generation of AI. We are looking for bright, motivated individuals to join our ranks and shape the future of superintelligent AI.

Key Responsibilities:

Develop and train advanced LLMs and VLMs, including multimodal architectures

Research and implement training methods for enhanced capabilities like instruction following and tool use

Design and optimize data pipelines and training systems for large-scale distributed training

Collaborate with cross-functional teams to integrate models into agentic AI systems

Evaluate model performance and communicate findings to stakeholders

Stay current with advancements in LLMs, VLMs, and related fields

Requirements:

Technical skills:

Strong programming skills (Python, Git)

Expertise in deep learning frameworks (PyTorch, JAX, TensorFlow)

Experience with large-scale distributed training of LLMs and VLMs

Hands-on experience with LLM training, alignment, and reinforcement learning

Knowledge of multimodal architectures and applications

Research skills:

Publications in top-tier AI conferences (e.g., NeurIPS, ICML, CVPR, ACL, ICCV)

Advanced degree (PhD or MSc) in a relevant field (e.g., ML, DL, NLP, CV)

Soft skills:

Excellent communication and presentation skills

Strong collaboration and teamwork skills

Passion for AI and problem-solving