Job Overview
Location
Remote
Job Type
Part-time
Category
Data Science
Date Posted
July 10, 2026
Full Job Description
đź“‹ Description
- • Collaborate with leading experts in AI, distributed systems, and protocols to develop state-of-the-art open language models, coding agents, and scientific discovery models.
- • Contribute to projects focused on democratizing AI and making it universally accessible through the Prime Intellect platform.
- • Gain practical experience in developing and deploying large-scale AI models using novel architectures and distributed training techniques across thousands of GPUs and smaller clusters.
- • Publish research papers and present findings at top-tier AI conferences.
- • Develop a strong network within the decentralized AI community.
🎯 Requirements
- • PhD students or postdoctoral researchers in computer science, mathematics, physics, neuroscience, or related fields.
- • Experienced software engineers with a strong interest in transitioning to AI research.
- • Self-taught individuals with a proven track record of exceptional technical contributions.
- • Candidates with experience in machine learning, distributed systems, or protocols.
🏖️ Benefits
- • Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco or Berlin.
- • Visa sponsorship and relocation assistance for international candidates.
- • Quarterly team off-sites, hackathons, conferences and learning opportunities.
- • Opportunity to work with a talented, hard-working and mission-driven team, united by a shared passion for leveraging technology to accelerate science and AI.
Skills & Technologies
See exactly how your profile matches this role — strengths, skill gaps, and what to do about them.
About Prime Intellect, Inc.
San Francisco–based startup building decentralized AI infrastructure that lets researchers pool compute and data to collaboratively train large models. Founded in 2023, the company offers open-source protocols and cloud orchestration tools that aggregate GPUs across providers, coordinate distributed training, and cryptographically verify contributions so participants share ownership and future rewards of the resulting models.
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