About the job
At Dexmate, we are an innovative robotics startup dedicated to developing versatile mobile robots capable of executing intricate manipulation tasks. We are seeking a passionate, skilled, and driven Research Scientist to join our founding team and help propel the capabilities of robotic manipulation. Ideal candidates will possess substantial expertise in machine learning and/or robotics. In this role, you will collaborate with a dynamic team of researchers and engineers, spearheading continual innovation and technological advancements within our organization. This is a full-time, on-site position located in Santa Clara, CA.
Key Responsibilities
- Design and develop advanced algorithms and methodologies for training AI models to improve robot dexterity.
- Engage in cutting-edge research across various domains, including Robotics, Reinforcement Learning, Imitation Learning, control systems, and perception.
- Handle large-scale machine learning systems and conduct extensive model training and fine-tuning.
- Craft and implement state-of-the-art learning-based algorithms for manipulation, navigation, and control on real robotic platforms.
- Collaborate with multidisciplinary teams to establish a diverse array of robust manipulation skills for robots.
Required Qualifications
- A Ph. D. in Robotics, Computer Science/Engineering, Electrical Engineering, Mechanical Engineering, or a related field, or equivalent research experience.
- A strong passion for robotics and the development of robotic products.
- Exceptional analytical, problem-solving, and communication abilities.
- A minimum of 3 years of experience conducting independent research.
- In-depth knowledge of state-of-the-art robot learning techniques, including reinforcement learning and imitation learning.
- A proven track record of research excellence, with publications in leading conferences and journals such as Science Robotics, IJRR, RSS, CoRL, ICRA, NeurIPS, ICML, ICLR, CVPR, among others.
- Proficient in Python programming.
- Experience with deep learning frameworks such as PyTorch, TensorFlow, or Jax.
- Hands-on experience with real robot experiments.
- Familiarity with robot simulation environments, including Isaac Gym, Isaac Sim, SAPIEN, MuJoCo, and Drake.
