Job Description

About the Role

We're looking for a VLA Algorithm Engineer working at the intersection of robotics, simulation, and world models to help advance our embodied AI foundation models. You'll build and optimize robotic simulation environments, develop large-scale synthetic training data pipelines, and improve how models trained in simulation transfer to real-world robots.

This role is ideal for someone who enjoys tackling challenging problems in robotics, reinforcement learning, imitation learning, and 3D vision, and wants to help build more capable, robust, and generalizable robotic systems.

Key Responsibilities

Robotics Simulation: Build and optimize simulation environments for robotic tasks, including manipulation, navigation, mobile manipulation, and humanoid robotics.

Embodied AI Foundation Models: Contribute to the training and optimization of embodied AI foundation models, including pretraining on large-scale egocentric human video and real-world robot data.

Multimodal Learning: Design approaches for incorporating multimodal inputs, including force and tactile sensing, and develop, train, evaluate, and optimize models that integrate these signals.

Simulation & Evaluation Infrastructure: Improve simulation and evaluation pipelines to reduce the sim-to-real gap and enhance model stability, robustness, and generalization across real-world environments.

Required Qualifications

At least 1 year of relevant professional experience or research experience in robotics, simulation, system identification, reinforcement learning (RL), imitation learning (IL), world models, 3D vision, or a related field.

Experience with robotics simulation, robotic learning, or related algorithm development.

Strong understanding of at least one relevant research area, such as reinforcement learning, imitation learning, system identification, world models, or 3D computer vision.

Preferred Qualifications

Simulation Platform Development: Hands-on experience developing or extending simulation platforms, with familiarity with the underlying architecture of tools such as NVIDIA Isaac Sim, Isaac Orbit, MuJoCo, PyBullet, or Genesis.

Large-Scale Synthetic Data Generation: Experience building synthetic datasets for robotics or computer vision, developing scalable data generation pipelines, or using diffusion models for large-scale synthetic data generation.

Sim-to-Real Transfer: Demonstrated experience transferring reinforcement learning or imitation learning policies from simulation to physical robotic arms or mobile robots. Familiarity with addressing simulation-to-reality differences in dynamics and observations through systematic compensation or adaptation techniques is a plus.

Default Benefits

Health insurance

Vision care

Dental coverage

401(k)

Paid holidays

PTO (Paid Time Off)

Sick leave