Engineering Lead – Multimodality Research

About the role

We are seeking a Member of Engineering to lead multimodality research for next‑generation AI systems. Based in Italy, you will shape a new research area that enables advanced models to understand and interact with visual information, working with large‑scale training infrastructure and a collaborative research team.

Key responsibilities

• Own the direction and execution of multimodality research initiatives, bringing advanced visual understanding capabilities into frontier AI models.
• Define and drive the multimodality roadmap and adoption across AI models.
• Build and launch initial image‑input capabilities for advanced coding and agentic systems.
• Design the evolution from adapter‑based approaches toward native multimodal architectures.
• Design and execute experiments across the full research lifecycle, including hypothesis formation, implementation, large‑scale training, and analysis.
• Collaborate with research and engineering teams on evaluation, architecture, data, and post‑training activities.
• Develop custom datasets and evaluation strategies to measure multimodal software engineering capabilities.
• Contribute to building a new research function from the ground up, balancing innovation with practical delivery.

Required profile

• Strong machine‑learning expertise with a research‑driven mindset.
• Proven experience building large‑scale AI systems and end‑to‑end training of production‑grade vision‑language models.
• Deep understanding of large language model fundamentals, transformers, and distributed training at scale.
• Advanced Python programming ability.
• Ability to work independently in ambiguous, fast‑paced environments.
• Strong experimental design and analysis skills.
• Experience collaborating across multidisciplinary teams.
• Demonstrated leadership or 0‑to‑1 building experience with broad ownership.

Required skills

• Python programming
• Vision‑Language Model (VLM) training
• Transformers
• Distributed training at scale
• Large Language Models
• Large‑scale AI system development
• Custom dataset creation
• Evaluation metric design for multimodal tasks