Technical Lead Manager, Physical AI

Scale AI•San Francisco, CA

1d•Onsite

About The Position

Scale AI is the data engine for the entire AI industry. Our mission is to accelerate the development of AI applications by providing organizations with the high-quality data they need. The Physical AI team at Scale is focused on the next frontier: building general AI that can reason and act in the physical world. By leveraging Scale’s massive data infrastructure, we are helping frontier labs build Foundation Models for Physical AI that will redefine the future of automation. As the Technical Lead Manager (TLM) for the Physical AI team of Scale, 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 (~60% of your time). Your primary focus will be the development and evaluation of Large-Scale Foundation Models (e.g VLAs, World models) that allow robots and AVs to generalize across diverse tasks, environments, and morphologies.

Requirements

Expert-level proficiency in PyTorch, with deep knowledge of Transformer architectures, Attention mechanisms, and Self-Supervised Learning.
Proven track record of working with Vision-Language Models (e.g., CLIP, PaLM-E) and adapting them for spatial reasoning or embodied tasks.
Experience with Diffusion Models for sequence generation or Generative World Models for predictive modeling.
Strong understanding of Physical AI stack, including imitation learning, reinforcement learning (RL), and multi-modal sensor fusion.
Experience with large-scale distributed training across GPU clusters and high-performance data loading.
1+ years of experience leading technical teams or projects in a research-intensive environment.

Nice To Haves

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

Responsibilities

Direct research into scaling laws for Physical AI, determining how to best utilize massive datasets for pre-training and fine-tuning generalist policies.
Develop novel methods for developing and evaluating models, including new Physical AI industry benchmarks.
Actively write code to implement, train and test SOTA architectures. Conduct research on Physical AI data collection, cross-embodiment training, and policy fine-tuning.
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.
Lead and grow a team of 4-6 elite Physical AI researchers, fostering a culture of high-velocity experimentation and rigorous evaluation.
Translate the latest research from NeurIPS, ICRA, and CVPR into production-ready features for Scale’s Physical AI partners.
Work with cross-functional teams (e.g Product and Operations) to bring our research breakthroughs into production.