AI Resident

About the Company

RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company’s robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability.

Responsibilities

• Conduct research and build systems for embodied physical intelligence
• Develop and evaluate methods in VLA, World Models, World Action Models, simulation, and RL
• Design and run experiments on robotics tasks involving perception, planning, control, and long-horizon behavior
• Build training and evaluation pipelines for large-scale embodied learning systems
• Work closely with research and engineering teams to move ideas from prototype to real or simulated robot platforms
• Explore how multimodal foundation models can improve robot capability in real deployment settings
• Contribute to technical reports, internal research discussions, and publications

Requirements

• Master’s, or PhD student, recent graduate, or early-career researcher/engineer in Computer Science, Robotics, Machine Learning, Electrical Engineering, or a related field
• Experience with modern ML frameworks such as PyTorch, JAX, or TensorFlow
• Experience using AI-assisted coding tools and agentic development workflows
• Ability to implement, debug, and evaluate research ideas in a fast-moving environment
• Strong engineering judgment and ability to productionize AI-assisted code

Preferred Qualifications

• Hands-on experience in robotic manipulation, mobile manipulation, or industrial robotics
• Experience training, fine-tuning, or evaluating multimodal or embodied models
• Experience with World Models, action-conditioned prediction, model-based learning, planning, or control
• Experience with simulation platforms such as Isaac Gym, Isaac Sim, MuJoCo, ManiSkill, or Habitat
• Experience with reinforcement learning, imitation learning, or post-training for robotic policies
• Experience working with real robot hardware and data collection systems

Benefits

• Company-provided lunch and dinner, a fully stocked kitchen, and team events
• Premium fitness center membership covered by the company
• Access to large-scale GPU clusters and production-grade infrastructure