Description
Why RoboForce
The AI Residency Program
Research Focus Areas
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Vision-Language-Action (VLA) models for general-purpose robotic behavior
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World Models for predictive modeling, planning, and long-horizon decision-making
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World Action Models for jointly modeling action and environment dynamics
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Simulation and sim-to-real transfer for scalable training, evaluation, and data generation
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Reinforcement learning, imitation learning, and policy optimization for embodied agents
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Multimodal learning across vision, language, proprioception, force, and action
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Learning systems for manipulation and real-world embodied interaction
What You’ll Do
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Conduct research and build systems for embodied physical intelligence
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Develop and evaluate methods in VLA, World Models, World Action Models, simulation, and RL
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Design and run experiments on robotics tasks involving perception, planning, control, and long-horizon behavior
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Build training and evaluation pipelines for large-scale embodied learning systems
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Work closely with research and engineering teams to move ideas from prototype to real or simulated robot platforms
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Explore how multimodal foundation models can improve robot capability in real deployment settings
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Contribute to technical reports, internal research discussions, and, where appropriate, publications
Basic Qualifications
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Master’s, or PhD student, recent graduate, or early-career researcher/engineer in Computer Science, Robotics, Machine Learning, Electrical Engineering, or a related field
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Experience with modern ML frameworks such as PyTorch, JAX, or TensorFlow
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Experience using AI-assisted coding tools and agentic development workflows to prototype, iterate, and build quickly
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Ability to implement, debug, and evaluate research ideas in a fast-moving environment
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Strong engineering judgment, including the ability to validate, refine, and productionize AI-assisted code
Preferred Qualifications
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Rich hands-on experience in robotic manipulation, mobile manipulation, or industrial robotics
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Experience training, fine-tuning, or evaluating multimodal or embodied models
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Experience with World Models, action-conditioned prediction, model-based learning, planning, or control
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Strong hands-on experience with simulation platforms such as Isaac Gym, Isaac Sim, MuJoCo, ManiSkill, Habitat, or similar systems
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Experience with reinforcement learning, imitation learning, or post-training for robotic policies
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Experience working with real robot hardware, data collection systems, evaluation workflows, or deployment pipelines
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Demonstrated technical initiative through research, open-source contributions, or high-impact engineering work
Compensation and Resources
- 3–6 months, full-time
- $10,000 monthly salary
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Company-provided lunch and dinner, a fully stocked kitchen, and team events
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Premium fitness center membership covered by the company
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