Research Engineer
Scaled Foundations
Scaled Foundations is an AI research and deployment company building general robot intelligence, and we are seeking a Research Engineer to join our team. We conduct focused research and engineering to build generalized robotics foundation models that work across form factors such as aerial, ground, manipulation, and others. We are looking for strong candidates who have a background in robotics and machine learning, with experience in areas like multimodal machine learning; synthetic data generation through simulation; LLMs; reinforcement learning and imitation learning for robotics; efficient training and inference.
Research Engineer Responsibilities
Design methods, tools, and infrastructure to push forward the state-of-the-art in robotics and foundation models.
Define research goals informed by practical engineering concerns.
Adapt standard machine learning models and frameworks to robotics applications.
Work with high-fidelity simulation platforms for synthetic data generation.
Develop sim2real and teleoperation pipelines for diverse tasks.
Design data loading and training pipelines for large scale foundation model training.
Contribute to experiments, including designing experimental details, writing reusable code, running model evaluations, and organizing results.
Contribute to publications and open-sourcing efforts.
Minimum Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
Research experience in machine learning, robotics, computer vision and natural language processing.
Experience with developing robotics algorithms or machine learning models at scale.
Programming experience in Python/C++. Good understanding of deep learning frameworks like Pytorch or Jax.
Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment.
Desired Qualifications
Master's/PhD in Robotics, Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
Direct experience in robotics, computer vision, or machine learning research. Experience with the Transformer model architecture.
First author publications at peer-reviewed AI and robotics conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICRA, IROS).
Experience with high fidelity simulation platforms such as AirSim, CARLA, Isaac Sim among others.
Experience building efficient robotics pipelines involving sensor fusion and model inference.
Good understanding of systems considerations and the ability to factor these into model choices.