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# VLA Algorithm Engineer - Simulation Data Generation & World Models

- **Company:** Maxinsights Corporation
- **Location:** Santa Clara, CA, United States
- **Contract:** Permanent contract
- **Skills:** Algorithm Design, Computer Vision, Reinforcement Learning, Multimodal Learning, Evaluation Pipelines, Mobile Robots, Transfer Learning, Large Language Models, Isaac Sim, Embodied AI, Data Generation
- **Published:** October 28, 2026
- **Apply:** https://www.thejobnetwork.com/job/018bbcfc-c2d3-40d9-8235-f122cb138827/vla-algorithm-engineer-simulation-data-generation-world-models

## About the Role

* At least 1 year of relevant professional experience or research experience in robotics, simulation, system identification, reinforcement learning (RL), imitation learning (IL), world models, 3D vision, or a related field.
 * Experience with robotics simulation, robotic learning, or related algorithm development.
 * Strong understanding of at least one relevant research area, such as reinforcement learning, imitation learning, system identification, world models, or 3D computer vision., * Simulation Platform Development: Hands-on experience developing or extending simulation platforms, with familiarity with the underlying architecture of tools such as NVIDIA Isaac Sim, Isaac Orbit, MuJoCo, PyBullet, or Genesis.
 * Large-Scale Synthetic Data Generation: Experience building synthetic datasets for robotics or computer vision, developing scalable data generation pipelines, or using diffusion models for large-scale synthetic data generation.
 * Sim-to-Real Transfer: Demonstrated experience transferring reinforcement learning or imitation learning policies from simulation to physical robotic arms or mobile robots. Familiarity with addressing simulation-to-reality differences in dynamics and observations through systematic compensation or adaptation techniques is a plus.

## Description

* Robotics Simulation: Build and optimize simulation environments for robotic tasks, including manipulation, navigation, mobile manipulation, and humanoid robotics.
 * Embodied AI Foundation Models: Contribute to the training and optimization of embodied AI foundation models, including pretraining on large-scale egocentric human video and real-world robot data.
 * Multimodal Learning: Design approaches for incorporating multimodal inputs, including force and tactile sensing, and develop, train, evaluate, and optimize models that integrate these signals.
 * Simulation & Evaluation Infrastructure: Improve simulation and evaluation pipelines to reduce the sim-to-real gap and enhance model stability, robustness, and generalization across real-world environments.