Technical Lead Manager, Physical AI
$249k - $311k • Remote • San Francisco, CA
Posted 2mo ago
Job Location
San Francisco, CA
Tech Stack
Remote Work Policy
Fully remote
Categories
Machine Learning Engineer
About the job
Scale AI is seeking a Technical Lead Manager for its Physical AI team, focusing on the development of general AI that can reason and act in the physical world. This role bridges cutting-edge Machine Learning research with physical robot deployment, leading a team of Research Engineers while remaining a hands-on technical contributor. The primary focus is on developing and evaluating Large-Scale Foundation Models, such as VLAs and World models, to enable robots and autonomous vehicles to generalize across diverse tasks and environments. The team leverages Scale's extensive data infrastructure to help build Foundation Models for Physical AI, aiming to redefine the future of automation.
Responsibilities
- Direct research into scaling laws for Physical AI to optimize massive datasets for pre-training and fine-tuning generalist policies.
- Develop novel methods for creating and evaluating Vision-Language Models (VLAs) and World models, including new industry benchmarks.
- Implement, train, and test state-of-the-art architectures, conducting research on Physical AI data collection, cross-embodiment training, and policy fine-tuning.
- Collaborate with internal labeling teams to design robotic-native data pipelines, utilizing VLMs for automated trajectory annotation and data synthesis.
- Partner with customers to advance the adoption of Scale data in the industry.
- Lead and mentor a team of 4-6 Physical AI researchers, fostering a culture of rapid experimentation and rigorous evaluation.
- Translate research from top AI/ML conferences into production-ready features for Physical AI partners.
- Align with cross-functional teams, including Product and Operations, to integrate research breakthroughs into production.
Requirements
- Expert-level proficiency in PyTorch, with deep knowledge of Transformer architectures, Attention mechanisms, and Self-Supervised Learning.
- Proven experience with Vision-Language Models (e.g., CLIP, PaLM-E) and their adaptation for spatial reasoning or embodied tasks.
- Experience with Diffusion Models for sequence generation or Generative World Models for predictive modeling.
- Strong understanding of the 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.
- First-author publications at top-tier AI/ML conferences (NeurIPS, CVPR, ICRA, CoRL) are a plus.
- Experience building models that generalize across different robot types (arms, mobile bases, humanoids) is a plus.
- Experience with high-fidelity simulators (e.g., Isaac Gym, MuJoCo) and sim-to-real transfer is a plus.
Benefits
- Base salary
- Equity compensation
- Comprehensive health, dental, and vision coverage
- Retirement benefits
- Learning and development stipend
- Generous PTO
- Commuter stipend