Lead Research Scientist (AI)

TL;DR

Lead Research Scientist (AI): Designing and training custom architectures that fuse multimodal inputs into a unified representation of user intent with an accent on combining LLMs, Computer Vision, and Graph Neural Networks for spatial relationships and unstructured collaboration. Focus on pioneering novel architectures, bridging academic theory with scalable production systems, and defining technical strategy for the "Intelligent Canvas."

Location: Amsterdam / Berlin / Yerevan / London

Company

hirify.global is a visual workspace for innovation that enables distributed teams of any size to build, design, and collaborate on an infinite canvas.

What you will do

• Pioneer novel architectures that fuse multimodal inputs (text, sketches, diagrams, screenshots, code) into a unified representation of user intent.
• Bridge academic theory from top-tier conferences (NeurIPS, ICLR, CVPR) with scalable, low-latency production systems through rapid prototyping.
• Define the technical strategy for Machine Learning, making high-stakes decisions on model selection and fine-tuning.
• Elevate the entire ML research engineering organization through rigorous code reviews, paper reading groups, and mentorship.
• Solve ambiguous, unsolved problems, such as generating diagrams from brain dumps or detecting agreement in spatial clusters of comments.

Requirements

• PhD or equivalent deep industrial experience in Computer Science, Math, or Physics, with 4+ years of professional experience shipping ML at scale (or Master’s with 7+ years industry experience, 2+ at Senior or Staff level).
• Public track record of patents, impactful open-source contributions, or first-author publications in top-tier conferences.
• Expertise in PyTorch or JAX to implement complex loss functions and debug distributed training on massive GPU clusters.
• Deep experience in at least one of the following: Generative AI (LLMs/Diffusion), Graph Neural Networks (GNNs), or Geometric Deep Learning.
• Strong engineering rigor to write clean, modular, production-ready code, understanding trade-offs between model accuracy and inference latency.
• Excellent communication skills to explain complex mathematical concepts to Product Managers, Designers, and Executives.

Nice to have

• Specific experience with Graph Neural Networks (GNNs) or Geometric Deep Learning.
• Hands-on experience building or fine-tuning Diffusion models for image/video generation or Multimodal LLMs.
• Performance optimization experience for constrained environments (e.g., ONNX, WebGpu, CoreML).
• Domain knowledge in Computational Creativity, HCI, or building tools for thought.

Culture & Benefits

• Competitive equity package.
• Health insurance for you and your family (for London), and corporate pension plan (for London).
• Lunch, snacks, and drinks provided in the office.
• Wellbeing benefit and WFH equipment allowance.
• Annual learning and development allowance to grow your skills and career.
• Opportunity to work for a globally diverse team.