BS, MS, or higher degree in Computer Science, Robotics, Mechanical Engineering, or related field, or equivalent practical experience.
Proficiency in Python and at least one deep learning library (e.g., PyTorch, TensorFlow, JAX).
Strong understanding of advanced deep learning techniques and architectures.
Experience with reinforcement learning and/or imitation learning.
Familiarity with distributed deep learning systems.
Responsibilities
Design, implement, and optimize deep learning models for robotic manipulation.
Model robotic manipulation processes for analysis, simulation, planning, and controls.
Collaborate with cross-functional teams to develop scalable manipulation solutions.
Integrate deep learning models into robotic platforms with inference and deployment teams.
Benefits
Opportunity to work on cutting-edge robotics technology.
Collaborative environment with cross-functional teams.
Direct impact on advancing robotics capabilities.
Engagement in real-world challenges and solutions.
Full Job Description
Position Overview
We are looking for a Deep Learning Engineer to develop and refine models for robotic manipulation. You will design, train, and deploy algorithms that enable robots to interact intelligently and adapt to their environments. Working closely with our machine learning, robotics, and research teams, you'll ensure these models are robust, efficient, and ready for real-world challenges. Your work will directly advance Skild AI's robotics capabilities, enabling robots to perform complex tasks autonomously. Responsibilities
Design, implement, and optimize deep learning models for robotic manipulation.
Model robotic manipulation processes to enable analysis, simulation, planning, and controls
Collaborate with cross-functional teams to develop scalable and generalizable manipulation solutions for deployment in robotic systems.
Work with inference, deployment, and application teams to integrate deep learning models seamlessly into robotic platforms.
Preferred Qualifications
BS, MS, or higher degree in Computer Science, Robotics, Mechanical 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.
Proficiency with advanced deep learning techniques and architectures as well as reinforcement learning and or imitation learning.
Experience with distributed deep learning systems.
