Technical Lead Manager, Physical AI

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. 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. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. The base salary range for this full-time position in the location of San Francisco is: $248,800—$311,000 USD PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all ap

AI/ML Excellence: Deep Learning Mastery with expert-level PyTorch, transformer architectures, attention mechanisms, and self-supervised learning. Experience 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: Embodied AI expertise including imitation learning, reinforcement learning (RL), and multi-modal sensor fusion. Infrastructure experience with large-scale distributed training across GPU clusters and efficient data loading. Leadership: 1+ years of experience leading technical teams or projects in a research-intensive environment. Nice-to-haves: publication record at top-tier AI/ML conferences; hardware generalization across robot types; sim-to-real experience with high-fidelity simulators (e.g., Isaac Gym, MuJoCo) and domain adaptation aspects.

Technical Leadership & Research: Model Scaling – direct research into scaling laws for Physical AI and how to best utilize massive datasets for pre-training and fine-tuning generalist policies. VLA and World model development – develop methods for building 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 data collection, cross-embodiment training, and policy fine-tuning. Data Strategy – collaborate with labeling teams to design robotic-native data pipelines, including use of vision-language models for automated trajectory annotation and data synthesis. Collaborate with customers to drive industry adoption of Scale data. Team Management & Execution: Mentorship – lead and grow a team of 4-6 elite Physical AI researchers; foster high-velocity experimentation and rigorous evaluation. Paper-to-Product – translate research from conferences into production-ready features for Scale’s partners. Cross-functional Alignment – work with Product and Operations to bring breakthroughs into production.