Applied Research – Scientific AI

🕒 October 19

🌐 United States, United Kingdom – Remote

🇺🇸 United States – Remote 🇬🇧 United Kingdom – Remote

🏄 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 and post-train agents for scientific research, including literature search, code execution, domain-tool use, and experiment selection • Work across autonomous research and computational science with environments and evaluations built around research partners' tools, data, and experimental feedback • Work directly with customers, users, or research partners while maintaining a substantive research agenda • Build long-horizon environments for literature search, scientific database queries, code and domain-tool execution, and experiment selection within fixed budgets • Convert research partners' workflows, datasets, and outcome criteria into trainable tasks, evaluations, and reward signals • Design verifiers for correctness, novelty, and reproducibility, including checks for label leakage, lookup shortcuts, and unsupported conclusions • Connect programmatic checks, simulations, domain models, and expert feedback to training; calibrate confidence estimates and define human-review requirements • Develop RL and multi-agent methods for proposing, testing, and critiquing hypotheses • Compare methods with strong single-agent baselines at equal compute and evaluate on held-out problems • Maintain reproducible code, data, logs, and experiment configurations; rerun results, measure noise, perform ablations, and release reusable environments, evaluations, and research code

🎯 Requirements

• Depth in RL, agents, or post-training systems and ability to acquire relevant scientific context • Prior work in autonomous research, ML for science, scientific computing, or reproducible computational research is useful but not required • 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 willingness to change direction when evidence changes • Strong Python • Ability to build artifacts that other researchers and engineers can run, inspect, and extend • Comfort owning ambiguous problems and keeping partners and teammates current • Evidence of exceptional work through publications, open-source systems, benchmarks, or shipped products

🏖️ 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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