Applied Research – Sovereign AI

🕒 October 19

🏄 California – Remote

🏄 California – Remote

💵 $150k - $300k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Artificial Intelligence

👻 Ghost score 5%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Prime Intellect

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1 - 10 employees

🤖 Artificial Intelligence

☁️ SaaS

Artificial Intelligence • SaaS • Cloud Computing

Prime Intellect is a company focused on democratizing AI development by providing scalable and decentralized computing resources for training models. Their platform allows users to find and share global compute resources, enabling the training of state-of-the-art models through distributed clusters. They promote the collective ownership of AI innovations, including language and scientific models. Prime Intellect also offers a range of GPU options to facilitate affordable and efficient model training. They aim to advance decentralized training research and open-source AI development on a global scale.

📋 Description

• Build post-training and agent systems in public-sector and regulated-industry settings. • Turn workflows into tasks, environments, evaluations, and reward signals. • Run post-training experiments against real baselines and deploy successful results. • Determine whether gains come from the model, data, harness, inference, or infrastructure. • Build private improvement loops within sensitive-data boundaries, from trace selection through deployed updates. • Train permissioned, traceable, recoverable tool-using domain agents evaluated against real procedures. • Develop high-assurance evaluations for reliability, calibration, robustness, human escalation, and compliance. • Own agents with explicit permissions, evidence trails, bounded tools, and human checkpoints. • Conduct research on how privacy, access, and infrastructure constraints affect learning. • Work directly with customers, users, or research partners while maintaining a substantive research agenda.

🎯 Requirements

• Relevant experience: RL/post-training, private ML systems, high-assurance evaluation, agent safety, regulated-domain ML, public-sector technical work, or deployment under strict data and infrastructure constraints. • Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops. • Strong experimental judgment: meaningful baselines, careful trace inspection, clear failure attribution, and a willingness to change direction when the evidence changes. • Strong Python and the ability to build artifacts that other researchers and engineers can run, inspect, and extend. • Comfort owning ambiguous problems and keeping partners and teammates current as you resolve them. • Publications, open-source systems, benchmarks, and shipped products are valued as evidence of exceptional work.

🏖️ Benefits

• Equity incentives • Flexible Work (San Francisco, New York, London, or remote) • Visa Sponsorship & relocation support • Professional Development budget • Team Off-sites & conference attendance

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