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Scale Logo Scale · IT - Software Development

Technical Lead Manager, Physical AI

United States, San Francisco Employment contract 248800.00 - 311000.00 USD / Year · Job Posted May 29, 2026
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Job Description

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.

Job Responsibility

  • 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
  • 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

Requirements

  • 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
  • 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 have

  • 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

What we offer

  • comprehensive health, dental and vision coverage
  • retirement benefits
  • a learning and development stipend
  • generous PTO
  • commuter stipend
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