Technical Lead Manager (Physical AI)

Join Scale AI, the data engine for the entire AI industry. As the Technical Lead Manager for the Physical AI team, you will bridge the gap between cutting-edge Machine Learning research and physical robot deployment. You will lead a high-performing team of Research Engineers while remaining a hands-on technical contributor. Your primary focus will be the development and evaluation of Large-Scale Foundation Models that allow robots and AVs to generalize across diverse tasks, environments, and morphologies.

• Lead a high-performing team of Research Engineers in the development and evaluation of Large-Scale Foundation Models for Physical AI.
• Direct research into scaling laws for Physical AI, determining how to best utilize massive datasets for pre-training and fine-tuning generalist policies.
• Translate the latest research from NeurIPS, ICRA, and CVPR into production-ready features for Scale’s Physical AI partners.
• VLM/VLA Experience: Proven track record of working with Vision-Language Models (e.g., CLIP, PaLM-E) and adapting them for spatial reasoning or embodied tasks
• Deep Learning Mastery: Expert-level proficiency in PyTorch, with deep knowledge of Transformer architectures, Attention mechanisms, and Self-Supervised Learning
• Generative AI: Experience with Diffusion Models for sequence generation or Generative World Models for predictive modeling
• Infrastructure: Experience with large-scale distributed training across GPU clusters and high-performance data loading
• Leadership: 1+ years of experience leading technical teams or projects in a research-intensive environment
• Embodied AI: Strong understanding of Physical AI stack, including imitation learning, reinforcement learning (RL), and multi-modal sensor fusion
• Publication Record: First-author publications at top-tier AI/ML conferences (NeurIPS, CVPR, ICRA, CoRL)
• Sim-to-Real: Experience with high-fidelity simulators (e.g., Isaac Gym, MuJoCo) and the nuances of physical domain adaptation
• Hardware Generalization: Experience building models that work across different robot types (arms, mobile bases, humanoids)