About this Research Scientist role at 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 looking for Research Scientists to lead the effort in developing the next generation of techniques to advance the capabilities of our robotic systems. We are looking for people with proven expertise in machine learning and/or robotics, who are passionate about pushing the boundaries of what is possible. You will collaborate with a team of talented researchers and engineers, and enhance our cohesive research strategy, driving ongoing innovation and technological advancements within the company. The ideal candidate will have a track record of discovering fundamental, scalable solutions, and a passion for seeing their research transform into impactful applications.
Responsibilities
- Develop new algorithms and methods for training general-purpose robot foundation models.
- Conduct cutting edge research across multiple disciplines (Perception, Robotics, RL / IL, Machine Learning, etc.).
- Work with large scale systems and large-scale model training.
Preferred Qualifications
- PhD degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
- Multiple years of experience in conducting research.
- Proficiency in Python and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
- Publications at top-tier ML, robotics, or CV conferences (e.g., NeurIPS, ICML, ICLR, CoRL, RSS, ICRA, CVPR, ECCV, ICCV).
- Deep technical knowledge, and research experience in deep learning, reinforcement learning, robotics, or computer vision.
- Deep understanding of state-of-the-art machine learning techniques and models.
- Experience working with robot systems.
- Experience working with large-scale model training and systems.
