Skild AI
Deep Learning Manipulation Engineer
Pittsburgh, San Mateo
Sponsorship not specified$100k-$300kDetected 97 days ago
PythonAlgorithmsMachine LearningDeep LearningTensorFlowPyTorchRoboticsMechanical DesignResearch
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.
- Working closely with our machine learning, robotics, and research teams, you'll ensure these models are robust, efficient, and ready for real-world challenges.
Responsibilities
- Collaborate with cross-functional teams to develop scalable and generalizable manipulation solutions for deployment in robotic systems.
- You will design, train, and deploy algorithms that enable robots to interact intelligently and adapt to their environments.
- Your work will directly advance Skild AI's robotics capabilities, enabling robots to perform complex tasks autonomously.
Nice to have
- BS, MS, or higher degree in Computer Science, Robotics, Mechanical Engineering, or a related field, or equivalent practical experience.
Compensation
- $100,000 - $300,000 USD
Benefits
- Design, implement, and optimize deep learning models for robotic manipulation.
- Work with inference, deployment, and application teams to integrate deep learning models seamlessly into robotic platforms.
- We are looking for a Deep Learning Engineer to develop and refine models for robotic manipulation.
Company info
- 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.
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This listing is sourced directly from Skild AI's careers page and normalized into a canonical job model.