Applied Research, Chip Design

🔥 15 hours ago

🌐 United States, United Kingdom – Remote

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

🏄 California – Remote

🏄 California – Remote

💵 $150k - $300k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

✍️ Designer

👻 Ghost score 3%

What it means: how likely this posting is sitting open without active hiring.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

WebsiteLinkedInAll Job Openings

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 agents, environments, evaluations, and post-training systems for semiconductor design and verification workflows • Turn workflows into tasks, environments, evaluations, and reward signals • Run post-training experiments against real baselines • Determine whether gains come from the model, data, harness, inference, or infrastructure • Build interactive verification environments where agents read specifications, write or repair RTL, invoke simulation or formal tools, interpret failures, and iterate toward verified results • Design rewards and evaluations combining functional correctness, tool efficiency, robustness, and performance/power/area constraints • Train long-horizon agents to recover from design and tool failures, preserve useful state, and generalize across blocks, designs, and constraint sets • Develop interactive environments where agents use EDA tools to modify designs, debug failures, and plan multi-step work • Develop verifiers and rewards grounded in tests, simulation, formal methods, design rules, and engineering outcomes • Experiment on credit assignment when feedback arrives late and several tools contribute • Create evaluations for correctness, recovery, and generalization • Work directly with customers, users, or research partners while maintaining a substantive research agenda

🎯 Requirements

• Depth in RL, agents, or post-training systems and ability to become fluent in semiconductor workflows • Prior work in design automation, compilers, formal methods, or ML for systems 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 such as 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