Sobre esta vaga de Deep Learning Manipulation Engineer na 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 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.