# Technical Lead Manager, Physical AI

**Company:** [Scale AI](https://hotfix.jobs/companies/scale-ai)
**Location:** San Francisco, CA
**Role:** Engineering Management
**Salary:** $249k – $311k/yr
**Experience:** 1+ years
**Skills:** PyTorch, Transformers, Attention Mechanisms, Self-Supervised Learning, Vision-Language Models, Clip, Palm-E, Diffusion Models, Imitation Learning, Reinforcement Learning, Multi-Modal Sensor Fusion, Gpu Clusters, Isaac Gym, Mujoco
**Posted:** 2026-05-08

> Leads a team of Physical AI researchers while contributing hands-on to developing large-scale foundation models like VLAs and world models for robots and AVs. Requires PyTorch expertise, VLM experience, embodied AI knowledge, and 1+ years leading technical teams.

## Job Description

## 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.

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