Job Description

About the Role

For a stealth robotics research startup, we are seeking a Robotics Researcher with a strong foundation in data-driven generalist models to advance our efforts in large-scale, multimodal data collection, curation, and model training for robotics. You will contribute to the development of systems that enable robots to generalize across tasks, environments, and sensor modalities.

Responsibilities

Design and implement pipelines for large-scale robotics data collection and annotation (video, egocentric, 3D, simulation). Develop and evaluate generalist models for perception, control, and world modeling using collected data. Automate dataset ingestion, cleaning, and integration from heterogeneous sources (simulation, real-world sensors, public datasets). Collaborate on benchmarking, dataset standardization, and metadata schema design for robotics datasets. Integrate multimodal data (RGB, depth, IMU, tactile) into unified model training workflows. Prototype and train models across manipulation, mobility, and navigation domains using state-of-the-art frameworks. Contribute to open-source toolkits, evaluation benchmarks, and internal research reports.

Requirements

Strong background in computer vision, machine learning, or embodied AI. Experience with model training (transformers, diffusion, or imitation/RL models). Proficiency in Python and familiarity with PyTorch, TensorFlow, or JAX. Experience working with large datasets (e.g., manipulation, simulation, egocentric). Understanding of ROS, Gazebo, Isaac Sim, or similar robotics ecosystems. Strong collaborative and documentation skills.

Preferred Qualifications

Prior work with foundation or generalist models (e.g., RT-X, Gato, Octo). Experience contributing to open robotics or dataset projects (Ego4D, ManiSkill, CALVIN). Familiarity with synthetic data generation, 3D scene reconstruction, or world modeling. Understanding of robotics benchmarking protocols and FAIR dataset principles.
Job Type: Full-time

Work Location: Remote