À propos de ce poste Research Engineer chez Skild AI
Company Overview
At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects.
Position Overview
We are hiring Research Engineers to develop scalable robotic systems aimed at achieving general-purpose robotic intelligence. In this role, you'll work with researchers and engineers to implement state-of-the-art machine learning and robot systems. The ideal candidate has a strong technical background, experience in turning research into practical solutions, and a drive to build impactful applications.
Responsibilities
- Develop and implement new algorithms for training and optimizing general-purpose robot foundation models.
- Design and engineer systems to scale research from prototypes to fully deployable solutions, working with large-scale systems and datasets.
- Conduct cutting edge research across multiple disciplines (Perception, Robotics, RL/ IL, Machine Learning, etc.).
- Collaborate with a multidisciplinary team, contributing to the integration of research insights into practical robotics applications.
- Optimize machine learning models for real-world robotic systems.
Preferred Qualifications
- BS, MS or PhD degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
- Proficiency in Python and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
- Strong track record of publishing papers and/or deploying real-world applications.
- Deep technical knowledge, and research experience in deep learning, reinforcement and/or imitation learning, robotics, or computer vision.
- Experience working with robot systems and large-scale model training.