Machine Learning Intern

San Mateo, Pittsburgh

Job Description

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 seeking Machine Learning Interns to support the development of reinforcement learning algorithms and experiments for robotics applications.. You will work closely with our robotics, research, and engineering teams to run experiments, analyze results, and optimize models for real-world performance.

This internship is a hands-on opportunity to gain experience applying cutting-edge ML techniques to robotic systems. Your work will help build intelligent, adaptable robots capable of learning and performing complex tasks autonomously.

Responsibilities

Develop and implement state-of-the-art reinforcement learning algorithms for robotic applications.
Design and conduct experiments to train RL models and conduct real-world tests.
Collaborate closely with researchers to explore novel methods of scaling up reinforcement learning model training.
Communicate effectively with inference, application, and deployment engineers to integrate RL models into robotic systems and iterate on methods to enable robust deployment.
Analyze and interpret experimental results, iterating on model design to achieve desired performance.

Preferred Qualifications

Currently pursuing a BS, MS, or higher degree in Computer Science, Robotics, Engineering, or a related field, or equivalent practical experience.
Proficiency in Python, C++, or similar and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
Understanding and practical experience with various reinforcement learning algorithms and techniques (model-free, model-based, multi-task, hierarchical, multi-agent, etc.).
Experience with physics simulation engines and tools for training RL.
Understanding of state-of-the-art machine learning techniques and models.

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