Technical Lead Manager, Physical AI

Key Responsibilities

Technical Leadership & Research

• Model Scaling: Direct research into scaling laws for Physical AI, determining how to best utilize massive datasets for pre-training and fine-tuning generalist policies.
• VLA and World model development: Develop novel methods for developing and evaluating models, including new Physical AI industry benchmarks.
• Hands-on Modeling: Actively write code to implement, train and test SOTA architectures. Conduct research on Physical AI data collection, cross-embodiment training, and policy fine-tuning.
• Data Strategy: Collaborate with internal labeling teams to design "robotic-native" data pipelines, including the use of VLMs for automated trajectory annotation and data synthesis.
• Collaborate closely with customers to drive the industry forward in using Scale data.

Team Management & Execution

• Mentorship: Lead and grow a team of 4-6 elite Physical AI researchers, fostering a culture of high-velocity experimentation and rigorous evaluation.
• Paper-to-Product: Translate the latest research from NeurIPS, ICRA, and CVPR into production-ready features for Scale’s Physical AI partners.
• Cross-functional Alignment: Work with cross-functional teams (e.g Product and Operations) to bring our research breakthroughs into production.

Required Qualifications

AI/ML Excellence

• Deep Learning Mastery: Expert-level proficiency in PyTorch, with deep knowledge of Transformer architectures, Attention mechanisms, and Self-Supervised Learning.
• 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.
• Generative AI: Experience with Diffusion Models for sequence generation or Generative World Models for predictive modeling.

Physical AI & Software Background

• Embodied AI: Strong understanding of Physical AI stack, including imitation learning, reinforcement learning (RL), and multi-modal sensor fusion.
• 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.

Nice to Haves

• Publication Record: First-author publications at top-tier AI/ML conferences (NeurIPS, CVPR, ICRA, CoRL).
• Hardware Generalization: Experience building models that work across different robot types (arms, mobile bases, humanoids).
• Sim-to-Real: Experience with high-fidelity simulators (e.g., Isaac Gym, MuJoCo) and the nuances of physical domain adaptation.