The posting
Why RoboForce
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.
The AI Residency Program
The AI Residency Program is designed for exceptional early-career researchers and engineers who want to tackle some of the hardest problems in robotics and AI. Residents will work alongside a deeply technical team on core challenges in embodied physical intelligence, including Vision-Language-Action (VLA) models, World Models, World Action Models, and 3D foundation models, as well as the full stack of real-world learning—from efficient data collection systems and simulation to reinforcement learning and deployment on physical robots.
This is a hands-on residency for people who want to do ambitious work with real consequences: building learning systems that connect perception, reasoning, and action in service of capable, deployable robots. What makes this program different is the direct connection between research and real-world deployment. Residents work with actual RoboForce robots, iterate quickly between simulation and physical execution, and contribute to systems designed for real use.
The problems are hard, the standards are high, and the goal is to build systems that matter outside the lab.
Research Focus Areas
As an AI Resident, you may contribute across several core areas:
- Vision-Language-Action (VLA) models for general-purpose robotic behavior
- World Models for predictive modeling, planning, and long-horizon decision-making
- World Action Models for jointly modeling action and environment dynamics
- Simulation and sim-to-real transfer for scalable training, evaluation, and data generation
- Reinforcement learning, imitation learning, and policy optimization for embodied agents
- Multimodal learning across vision, language, proprioception, force, and action
- Learning systems for manipulation and real-world embodied interaction
What You’ll Do
- 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, where appropriate, publications
Basic Qualifications
- 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 to prototype, iterate, and build quickly
- Ability to implement, debug, and evaluate research ideas in a fast-moving environment
- Strong engineering judgment, including the ability to validate, refine, and productionize AI-assisted code
Preferred Qualifications
- Rich 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
- Strong hands-on experience with simulation platforms such as Isaac Gym, Isaac Sim, MuJoCo, ManiSkill, Habitat, or similar systems
- Experience with reinforcement learning, imitation learning, or post-training for robotic policies
- Experience working with real robot hardware, data collection systems, evaluation workflows, or deployment pipelines
- Demonstrated technical initiative through research, open-source contributions, or high-impact engineering work
Compensation and Resources
Duration:
- 3–6 months, full-time
Compensation:
- $10,000 monthly salary
Benefits:
- Company-provided lunch and dinner, a fully stocked kitchen, and team events
- Premium fitness center membership covered by the company
Resources:
Access to large-scale GPU clusters and production-grade infrastructure, with dedicated support to enable fast, uninterrupted experimentation on ambitious robotics and AI workloads
