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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.
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Position Overview
We are looking for a Research Engineer who is passionate about delivering results in the real world. As a member of the post-training team, you’ll be responsible for improving Skild foundation models and deploying them onto robots in the field. You’ll work with customers and deployment data to deliver reliable robot behavior in real-world environments, ensuring our systems are safe, efficient, and robust under operational constraints.
We work across the final mile of robotics: turning strong lab performance into dependable customer deployments and continuous improvement. By bridging this gap, you will define the standard for how autonomous systems scale from experimental prototypes into indispensable global infrastructure.
Responsibilities
- Research, post-train and evaluate large deep learning models for robotic manipulation tasks.
- Develop frameworks to continuously improve robot behaviors.
- Collaborate closely with our product teams to source and iterate on customer requirements to ensure technical alignment for on-site deployments.
- Own scenario setup and data collection methodologies, managing the operational lifecycle for unique customer use-cases.
- Work with our robotics teams to maintain and ensure robots are deployment-ready and execute full-stack software and hardware deployments at customer sites.
- Build robust testing and evaluation pipelines for tracking model improvements and corner cases.
Preferred Qualifications
- BS, MS or PhD degree in Computer Science, Robotics, 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.
- Deep technical knowledge in computer vision and deep learning for robotics, including reinforcement and imitation learning.
- Prior experience working with large-scale model training.
- Prior experience developing and deploying ML models or software on real robots.
- Prior experience with ROS/ROS2 or other robotics middleware platforms.
- Bonus if you have experience working with customers and deploying real-world robotics applications.
- Bonus if you have experience leveraging LLMs to accelerate development and solve complex engineering problems.
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