Why this role stands out
As a Senior / Staff AI Research Scientist at RoboForce, you'll drive innovation in Physical AI, developing groundbreaking foundation models for robotic embodied intelligence and contributing to a company at the forefront of robotics. This hybrid role is ideal for experienced AI researchers passionate about building intelligent systems that can understand, plan, and act in complex physical environments, offering a unique opportunity to shape the future of robotics.
Quick overview
Job description
Why RoboForce
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Design and deploy vision-language(-action) models (VLM/VLA) for contextual understanding and generalized robot action policies.
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Develop and train world models for action-conditioned prediction, long-horizon planning, and environment simulation — enabling robots to reason about the consequences of their actions before execution.
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Research approaches to improve world model fidelity using multi-modal inputs including vision, language, proprioception, and spatial representations.
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Develop foundation models with spatial reasoning capabilities to achieve high-precision robotic actions.
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Integrate multi-modal data sources (vision, language, speech, etc.) to enable natural human-robot communication.
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Optimize and deploy models as production-grade solutions on RoboForce robotic platforms.
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PhD degree in Machine Learning, Robotics, or related field, or Master's degree with 4+ years of relevant experience.
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Proficiency in Python and deep learning frameworks (e.g., PyTorch, JAX).
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Expertise in large foundation models (VLM, VLA, etc.).
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Strong understanding of world model architectures and action-conditioned generative modeling for robot learning.
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Decent understanding of multimodal models, modern ML architectures (transformers, diffusion models, etc.).
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Requires 5 days/week in-office collaboration with the teams.
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Experience with video generation or prediction models (e.g., diffusion-based video models, autoregressive video transformers) and their application to world modeling or synthetic data generation for robot learning.
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Strong publication record at top conferences (NeurIPS, ICML, CVPR, ICCV, CoRL, ICRA, or equivalent).
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Expertise in neural network deployment (e.g., TensorRT) and GPU programming with CUDA.
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Proven ability to design scalable experimentation and data pipelines.
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Competitive stock options/equity programs.
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Health, dental, and vision insurance, 401(k) plan.
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Visa sponsorship and green card support for qualified candidates.
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Lunches and dinners, a fully stocked kitchen, and regular team-building events.
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