Role Overview
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.
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.