Software Engineer - India

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;

Software Engineer Responsibilities

• Design methods, tools, and infrastructure to push forward the state of the art in robotics and foundation models. 

• Implement novel perception and navigation algorithms for aerial and wheeled agents.

• Design robust and optimised large scale data generation, DL training and inference pipelines.

• Work with high fidelity simulation platforms for synthetic data generation.

• Contribute to publications and open-sourcing efforts.

Minimum Qualifications

• Experience in machine learning, robotics or building 0-1 software systems.

• Experience handling large scale data.

• 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.

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.