AI Resident

RoboForce•Milpitas, CA

4h•Onsite

About The Position

The AI Residency Program at RoboForce is designed for exceptional early-career researchers and engineers who are eager to tackle challenging problems in robotics and AI. As a resident, you will collaborate with a highly technical team on fundamental issues in embodied intelligence, including Vision-Language-Action (VLA) models, world models, reinforcement learning, simulation, and real-world robot learning. This is a hands-on residency for individuals committed to ambitious work with tangible outcomes: building learning systems that integrate perception, reasoning, and action to create capable, deployable robots. A distinctive feature of this program is the direct link between research and practical deployment. Residents will work with actual RoboForce robots, rapidly iterate between simulation and physical execution, and contribute to systems designed for real-world application. The program maintains high standards, with the ultimate goal of developing systems that have significant impact beyond the laboratory.

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 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

Nice To Haves

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

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, where appropriate, publications