# Member of Technical Staff - Head of Robotics at Embedding VC > Onsite · United States · Director+ · Software engineering · open. Source: https://alderhq.app/jobs/ashby%3Aembedding-vc%3A9fbbfbd3-40e9-4bcb-8a1e-069ba4c5e2fe (read 2026-11-04). ## Facts (observed) - Company: [Embedding VC](https://alderhq.app/companies/embedding-vc) - Location text: San Francisco Bay Area - Where it can be done from (inferred): Onsite · United States (evidence: "Location: San Francisco Bay Area") - Posted: 2026-07-27 (100 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/embedding-vc/9fbbfbd3-40e9-4bcb-8a1e-069ba4c5e2fe - Skills listed (inferred): Python ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (SF Bay Area, n 66): p25 $257k, median $289k, p75 $333k ## Description (the employer's text) Introducing Moonlake, AI for creating world simulations. ### About Moonlake Moonlake is building the frontier of AI-powered world simulation. We create systems that generate, simulate, and reason over rich 3D environments for robotics, embodied AI, and interactive applications. Our platform enables the creation of digital worlds, synthetic environments, and scalable simulation infrastructure used to train the next generation of intelligent systems. Our work sits at the intersection of: Robotics Physical AI World Models Simulation Infrastructure Synthetic Data Generation Embodied Intelligence Moonlake has raised $28M in seed funding from NVIDIA Ventures, Threshold Ventures, AIX Ventures, and notable angels including Naval Ravikant and Jeff Dean. Our mission is to build the foundational infrastructure that enables robots to learn, reason, and operate effectively in the physical world. ### The Role We are looking for a Member of Technical Staff – Robotics to help build the bridge between simulation, world models, and real-world robotic systems. This role spans the full robotics stack—from evaluating foundation models and policies in simulation, to training world models, to deploying and operating physical robots. You will work closely with researchers and engineers developing next-generation simulation environments and AI systems, while ensuring those capabilities transfer successfully into real-world robotic platforms. This is a highly hands-on role combining robotics engineering, machine learning, simulation, and hardware deployment. ### What You'll Do ### Evaluate Robot Foundation Models & Policies Benchmark and evaluate robot foundation models in simulated environments Design evaluation frameworks for robotic reasoning, planning, manipulation, and navigation Measure generalization, robustness, and task performance across diverse scenarios Build infrastructure for large-scale simulation-based testing and validation ### Train World Models for Robotics Develop and train world models that enable robots to understand and predict environment dynamics Build systems that learn from multimodal robot data including vision, depth, state, and actions Improve environment understanding, forecasting, and decision-making capabilities Work closely with simulation and AI teams to advance robotic world modeling systems ### Build Real-World Robot Learning Pipelines Collect and curate real-world robotics datasets Train and fine-tune models using both simulated and physical robot data Improve sim-to-real transfer for robotic policies and world models Develop workflows connecting simulation, training infrastructure, and deployed robotic systems ### Deploy and Operate Physical Robots Set up, integrate, and maintain robotic hardware platforms Bring learned policies and world models onto real robotic systems Debug hardware, software, sensing, and control issues Develop deployment pipelines for testing, validation, and continuous improvement Work directly with robotic manipulators, mobile robots, sensors, and compute systems ### Areas of Focus ### Robot Foundation Models Policy evaluation Model benchmarking Simulation-based testing Generalization analysis Performance measurement ### World Models Environment modeling Predictive systems Representation learning Multimodal learning Model-based reasoning ### Simulation Robotics simulators Digital twins Synthetic environments Sim-to-real transfer Evaluation infrastructure ### Robotics Systems Robot setup and integration Sensors and perception systems Robot control Hardware debugging Deployment workflows ### What We're Looking For Strong background in robotics, embodied AI, machine learning, or related fields Experience working with physical robotic systems Experience with robotic simulation platforms such as Isaac Sim, MuJoCo, Habitat, Gazebo, or similar Familiarity with robot learning, foundation models, or world models Strong software engineering skills in Python and robotics tooling Experience deploying software onto real robotic hardware Ability to debug across hardware, software, and machine learning systems Comfort working in a fast-moving research and engineering environment --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aembedding-vc%3A9fbbfbd3-40e9-4bcb-8a1e-069ba4c5e2fe and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.