Skild AI

Deep Learning Manipulation Engineer

Pittsburgh, San Mateo · Work style not stated

Posted over 6 months ago: check it’s still open

Apply on the employer’s site ↗See how well you match this job

You apply directly with the employer. ROLIVA does not submit applications from this page and is not the employer.

About the role

Description from the employer’s posting. Check the employer’s page for the current version.

  • EmployerSkild AI
  • LocationPittsburgh, San Mateo
  • Work styleWork style not stated
  • Employer posted

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.

Show full description

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.

Base Salary Range

$100,000—$300,000 USD

Staying safe while you apply

  • Never pay to apply, interview or accept an offer. ROLIVA never asks you for payment to apply to a job.
  • Apply only through the official “Apply on the employer’s site” button, on the employer’s own site, and check that a recruiter’s email uses that employer’s own domain rather than a free email address.
  • Be cautious of an offer made with no real interview, or pressure to decide quickly or share financial details early.

Does something look wrong with this listing? Use “Report broken link” in the role details, or read ROLIVA’s full job-safety guidance ↗.

Preparing for this role with ROLIVA

  • Check the fit.

    Compare the requirements with the experience you have confirmed, including gaps.

  • Prepare honestly.

    Build interview stories and application drafts from your own evidence.

  • Keep track.

    Save the role and record your own application status and follow-ups.

Create a free workspace or log in.

At a glance

  • Employer posted
  • Added to ROLIVA
  • Last checked

Skills in this posting

Skills ROLIVA found in the employer’s own description above. A mention is not a requirement.

Python Machine learning PyTorch TensorFlow

Explore similar roles

Similar jobs

Skild AI

Research Engineer

Pittsburgh, San Mateo

Work style not stated

Skild AI

Robot Operator

San Mateo

Work style not stated