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)
Familiarity with advanced deep learning techniques and architectures, reinforcement learning, or imitation learning
Experience 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 create scalable manipulation solutions
Integrate deep learning models into robotic platforms with deployment teams
Benefits
Flexible work environment
Opportunities for professional growth and development
Participation in innovative projects
Collaborative and diverse team culture
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.
