The Opentensor Foundation, creators of the Bittensor and the TAO cryptocurrency, is building the infrastructure for decentralized AI at internet scale. We support not only the core development of the Opentensor Foundation itself, but also the broader Bittensor ecosystem—including its growing network of independently operated subnets.

By applying to this position, you’ll be considered for opportunities both within the Foundation and across the wider Bittensor network. Whether you're contributing to foundational protocol R&D or helping advance frontier AI models in a subnet, you’ll be working on the cutting edge of decentralized machine intelligence.

The Role

We are looking for a Research Engineer with deep expertise in distributed training to help shape the future of AI infrastructure across decentralized systems. In this role, you’ll help design scalable training solutions that harness compute from globally distributed, permissionless participants.

If you’re excited by the idea of training state-of-the-art models on decentralized infrastructure—and building the systems that make it possible—this is a rare opportunity to do exactly that.

Responsibilities:

Drive innovative research efforts focused on building a large-scale, secure, and dependable system for orchestrating decentralized AI model training.
Continuously refine and enhance AI workload efficiency by applying cutting-edge techniques in compute and memory optimization.
Actively contribute to shaping our open-source tools and libraries that support scalable, distributed training of machine learning models.
Share breakthroughs and research findings with the broader community through publications in premier conferences like NeurIPS.
Keep a close eye on emerging trends in ML infrastructure, decentralized compute, and tooling—proactively identifying ways to evolve and improve the platform’s performance and developer experience.

Requirements:

Proven expertise in AI and machine learning engineering, with a track record of building and scaling complete pipelines for training and deploying large-scale AI models.
In-depth knowledge of distributed training methodologies and frameworks such as PyTorch Distributed, DeepSpeed, Ray, and MosaicML’s LLM Foundry, with a focus on enhancing training efficiency and system scalability.
Hands-on experience training large models at scale, leveraging advanced parallelism strategies including data, tensor, and pipeline parallel techniques.
Strong grasp of modern MLOps workflows, including model lifecycle management, experiment logging, and automation through CI/CD processes.
Deeply motivated by the mission to push the boundaries of decentralized AI training and make cutting-edge AI technology more accessible to a global community of developers and researchers.

What We Offer:

The opportunity to work remotely with a flexible schedule.
A collaborative and innovative work environment.
The chance to be part of a team that's making significant contributions to the field of artificial intelligence.
Competitive compensation, including token incentives.

Bittensor

Research Engineer - Distributed Training