AI Resident

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