Embedded GPU Software Engineer at Hiveon

About us

Today, Hiveon is a product company focused on developing infrastructure for new-era hardware businesses, from mining to energy and AI.

It all started in 2017 with a private project released by a few mining enthusiasts. Our Hive OS exploded in popularity, reaching 100k+ active users within the first six months without any investments or marketing costs.

We then expanded to an ecosystem, including a Pool (the second Ethereum pool in the world in 2021), ASIC Firmware (a low-level hacking solution to get more out of their devices), and became the leading global platform for cryptocurrency mining.

Since 2021, mining has become more than just hardware. And Hiveon is not only about mining. We provide the infrastructure for HPC businesses, helping to build, simplify, and automate operations in the energy and AI markets.

Let the numbers speak instead of us:
Geo - worldwide
Released products in 7 years - 6
Upcoming products - 2
Active users at peak - 2+ million
Connected devices - 5+ million
Mobile users - 600,000+
And all this with a team of less than 90 people.

Keep an eye on our upcoming announcements ⭐️

Currently we're looking for Embedded GPU Software Engineer to contribute to our new AI/ML product.

What's the concept? We aim to decentralize and enhance the democratization of GPU computing, assisting individuals requiring resources for AI and ML-related tasks. We want to make computing resources more accessible and secure.

For whom? Individuals seeking for hardware resources for AI and ML tasks.

Key Responsibilities:

• Design and implement solutions utilizing GPU technologies.
• Write and optimize code.
• Troubleshoot, debug, and upgrade existing software.
• Develop and manage virtualization strategies and resources within containers.
• Ensure isolation and security within containerized environments.
• Recommend and execute improvements.
• Collaborate with CTO and team members to make GPU great again.

Requirements:

• Experience with GPU software design and programming
• Experience working directly with hardware systems.
• Deep knowledge of programming on Rust or C++.
• Experience with pass-through.
• Knowledge of virtualization concepts and their applications.
• Proficient in Docker and containerization best practices.
• Experience with isolation and security measures in containers.
• Strong communication skills.

Nice to have:

• Experience or interest in utilizing machine learning workloads.