Deep Learning Manipulation Engineer
- Pay
- $100,000 – $300,000 per year
- Location
- Pittsburgh, San Mateo · On-site
- Job type
- Full-time
- Level
- Mid-level
- Education
- Bachelor's degree
- Listed
- 30 days ago
Skills & highlights for this role
- deep learning
- PyTorch
- reinforcement learning
- imitation learning
Also useful
- TensorFlow
- JAX
- distributed deep learning
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.
Pay and benefits
Base Salary Range $100,000—$300,000 USD
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.
From Skild AI's careers page, first listed on Rolev.ai on October 10, 2026. Rolev.ai is not the employer.
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Common questions
What is the salary for the Deep Learning Manipulation Engineer role at Skild AI?
The Deep Learning Manipulation Engineer posting at Skild AI states compensation of $100,000 – $300,000 per year. It is listed as a full-time position. Final pay may depend on location, experience, and interview evaluation on Skild AI's official career site.
Where is the Deep Learning Manipulation Engineer job at Skild AI located?
This Deep Learning Manipulation Engineer position at Skild AI is located in Pittsburgh, San Mateo (On-site). Always check Skild AI's application page for specific state eligibility or relocation policies.
What qualifications are required for Deep Learning Manipulation Engineer at Skild AI?
For the Deep Learning Manipulation Engineer role, Skild AI highlights a bachelor's degree; experience with deep learning, PyTorch, reinforcement learning, imitation learning. Rolev.ai can score your existing resume against these requirements and show any skill gaps before you apply.
How do I apply for the Deep Learning Manipulation Engineer position at Skild AI?
This listing links directly to Skild AI's Greenhouse application page. You can apply directly on Skild AI's site, or use Rolev.ai to check your resume fit, generate a zero-hallucination tailored resume and cover letter for this posting, and autofill the Greenhouse application form in 1 click with the Rolev.ai Chrome extension. You always review the form and press Submit yourself.