Samsung

Background

Samsung, a global leader in consumer electronics and semiconductors, has long invested in artificial intelligence across hardware optimization, edge computing, mobile experiences, and smart appliances. However, as software and AI talent increasingly drive innovation, Samsung’s leadership realized that traditional organizational models—optimized for hardware-centric R&D—were no longer suitable for nurturing top-tier AI talent at scale.

They approached Valley Bridge Partners with a specific mandate:

"Help us build the org and talent DNA that attracts and retains world-class AI software engineers and researchers."

The Challenge

• Siloed Structures: AI teams were fragmented across divisions with inconsistent reporting lines and duplicative research efforts.
• Undefined Career Tracks: Samsung lacked clearly articulated career growth paths for AI researchers vs applied ML engineers.
• Poor Talent Signal: Current hiring and promotion frameworks were hardware-biased, undervaluing publishing, model-building, and open-source contributions.
• Global Inconsistency: Hiring practices in Seoul, Austin, and Bangalore varied widely in expectations and rigor.

Our Approach

Valley Bridge Partners deployed a three-track strategic engagement:

1. Organizational Blueprint

We benchmarked organizational models from Meta AI, Google DeepMind, and Microsoft Research, and worked with Samsung's leadership to define:

• A hybrid-center model that balanced a central AI lab with embedded vertical squads.
• A Research Track and Applied Engineering Track with tailored career ladders.
• A Global AI Council to coordinate research themes, roadmaps, and resource allocation across business units.

2. Talent Evaluation Framework

We redesigned Samsung’s AI hiring rubric to focus on:

• Signal Strength Mapping: Paper authorship, GitHub contribution heatmaps, Kaggle scores, and internal IP creation.
• Skill Archetyping: Differentiated expectations for core ML engineers, platform enablers, and foundational researchers.
• Behavioral Markers: Introduced structured assessments to evaluate curiosity, collaborative depth, and problem decomposition skills.

3. Talent Intelligence System

We recommended and helped implement a Talent Graph System—a platform that maps existing internal AI talent by skills, contribution area, and collaboration density to:

• Inform promotion panels with AI-native metrics
• Enable smart team formation for emerging research projects
• Identify succession gaps and upskilling priorities

‍