Skild AI

Research Engineer, Post-training & Deployment

San Mateo, CA

Sponsorship not specified$100k-$300kDetected 98 days ago

PythonFull-Stack DevelopmentMachine LearningDeep LearningTensorFlowPyTorchComputer VisionLLMsRoboticsResearch

About the role

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.
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.

Develop frameworks to continuously improve robot behaviors.
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.

BS, MS or PhD degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
Prior experience working with large-scale model training.
Prior experience with ROS/ROS2 or other robotics middleware platforms.

Research, post-train and evaluate large deep learning models for robotic manipulation tasks.

At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing.
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.
We are looking for a Research Engineer who is passionate about delivering results in the real world.
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.

This listing is sourced directly from Skild AI's careers page and normalized into a canonical job model.