Applied Research, Chip Design

🕒 Outubro 19

🌐 Estados Unidos, Reino Unido – Remoto

🇺🇸 Estados Unidos – Remoto (EUA) 🇬🇧 Reino Unido – Remoto (RU)

🏄 California – Remoto

🏄 California – Remote

💵 $150.000 - $300.000 / ano

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

✍️ Designer

👻 Score fantasma 5%

O que significa: a chance de ninguém estar realmente contratando para esta vaga.Como ler: 60% ou mais significa que achamos que é uma vaga fantasma. Pontuações mais baixas preocupam menos.Por que essa pontuação:

• Aberta há 23 dias.
• As vagas desta empresa costumam ser preenchidas em 27 dias.

Candidatar-se

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Prime Intellect

SiteLinkedInTodas as Vagas

1 - 10 funcionários

🤖 Inteligência Artificial

☁️ SaaS

Artificial Intelligence • SaaS • Cloud Computing

Prime Intellect é uma empresa focada em democratizar o desenvolvimento de IA, fornecendo recursos computacionais escaláveis e descentralizados para treinamento de modelos. Sua plataforma permite que os usuários encontrem e compartilhem recursos computacionais globais, possibilitando o treinamento de modelos de ponta através de clusters distribuídos. Eles promovem a propriedade coletiva das inovações de IA, incluindo modelos de linguagem e cientificos. A Prime Intellect também oferece uma gama de opções de GPU para facilitar o treinamento de modelos de forma acessível e eficiente. Eles visam avançar na pesquisa de treinamentos descentralizados e no desenvolvimento de IA de código aberto em escala global.

Descrição

• 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

🎯 Requisitos

• 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

🏖️ Benefícios

• 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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