About the role

Aplyr's Quick Take

This role is for a Deep Learning Engineer focused on creating and optimizing models for robotic manipulation. You'll work on designing, training, and deploying algorithms that help robots interact with their environments effectively, collaborating with various teams to ensure these models are practical for real-world use.

Good fit

Ideal candidates have a background in computer science, robotics, or mechanical engineering, with experience in deep learning frameworks like PyTorch or TensorFlow. A collaborative mindset and a passion for innovative technology are essential.

Worth noting

The salary range is quite high, suggesting a competitive environment or a strong emphasis on attracting top talent. The role seems to prioritize demonstrated skills over formal experience, which could appeal to those with non-traditional backgrounds.

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.

Base Salary Range

$100,000—$300,000 USD

Skills & Tags

python go machine learning ai ios data design

Aplyr's read

Skild AI is at the forefront of integrating AI into recruitment, attracting diverse roles from robotics to software engineering, ideal for tech enthusiasts.
Synthesized from recent postings & public sources

What's promising

•Skild AI offers innovative AI solutions for streamlining recruitment processes.
•The company hires for a wide range of technical and creative roles.
•Skild AI is involved in cutting-edge technology, especially in AI and robotics.

What to watch

•The company may face competition from established HR tech giants.
•Limited public information about its financial stability and growth metrics.
•Potential job seekers might find the company's niche focus limiting.

Why Skild AI

•Skild AI specializes in AI-driven talent assessment, setting it apart in HR tech.
•The company integrates robotics into its recruitment solutions.
•Skild AI's diverse role offerings reflect its commitment to multidisciplinary innovation.

Aplyr’s read is generated by AI from public sources. Was it useful?