Develop and refine deep learning models for robotic manipulation at a pioneering AI startup.
Posted by employer 1 year ago
First seen on Joblaze 1 week ago
Last verified on the company career page 1 day ago
Skills & Technologies
Requirements
- Education
- Bachelor's degree
Not disclosed in this posting: years of experience, work arrangement, visa sponsorship.
Joblaze summary
In this role, the Deep Learning Manipulation Engineer at Skild AI focuses on developing and optimizing algorithms that allow robots to intelligently interact with their environments. Key skills include proficiency in Python and experience with deep learning frameworks like PyTorch or TensorFlow, alongside knowledge of advanced techniques such as reinforcement learning. This position is ideal for candidates with a strong technical background in computer science or robotics, whether they are seasoned professionals or recent graduates. Skild AI fosters a collaborative environment, encouraging innovative contributions to their cutting-edge robotics projects.
Joblaze insights
- Listed 9 days ago on Joblaze — tracked directly from Skild AI's career page.
- Skild AI has 63 other open roles including Manufacturing Engineering Manager, Senior Electrical Engineer , Operations Program Manager – Data Infrastructure - India.
- 6527 active Python roles on Joblaze right now.
- 481 active PyTorch roles on Joblaze right now.
- 223 active TensorFlow roles on Joblaze right now.
- Salary band is below the typical range for AI/ML roles (median ~$188,000).
- Pays more than 8% of the 3228 salaried Python roles we track (median $160,000). See Python salary trends
Quick facts
- What's the salary range?
- Skild AI lists $100,000–$300,000 for this role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: JAX, PyTorch, Python, TensorFlow.
- What seniority level is this role?
- Skild AI targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Deep Learning Manipulation Engineer role at Skild AI.
From the original posting
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.