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Bronco AI

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About

Bronco is an applied AI lab focused on accelerating chip development. They provide AI tools to augment and accelerate design verification (DV) for chip teams. Bronco's technology is deployed in industry, catching bugs and providing velocity advantages. They are backed by investors including 8VC, Elad Gil, SV Angel, YC, and Contrary. Bronco aims to close the verification gap as chips become more specialized and timelines shrink. Their AI accelerates mundane DV workflows, reducing hours to minutes or minutes to seconds.

Platform
Web
Task
chip verification

Features

accelerating dv workflows

clearing backlogs

bug catching

ai-driven design verification

Job Opportunities

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Bronco AI

Founding ML Research Engineer

Bronco AI accelerates chip development using AI-powered tools for design verification, catching bugs and speeding up workflows.

engineeringonsiteSan Francisco, US
200,000 USD per year (Bronco HQ) - 300,000 USD per year (Bronco HQ)
full-time

Benefits:

  • Competitive salary and equity package

  • Comprehensive benefits including health, vision, and dental coverage

  • High-ownership, high-velocity environment with rapid learning opportunities

  • Chance to work on a profound technical problem that, if solved, will accelerate the future of technology development and civilization

Education Requirements:

  • A Bachelor's degree in Computer Science, Software Engineering, or related field; advanced degree preferred

Experience Requirements:

  • You are an exceptional researcher who has designed large-scale ML systems

  • Experience building LLM applications and agentic LLM systems

  • Familiarity with cloud computing platforms and distributed systems

  • Have strong intuition about computer architecture to quickly understand chip design and verification tasks

  • Strong communication abilities and experience working in cross-functional teams

Other Requirements:

  • Strong proficiency in Python and AI/ML frameworks like PyTorch

Responsibilities:

  • Design and implement core components of our AI-powered engineering systems

  • Integrate classical algorithms and deep learning-based approaches across modalities

  • Utilize our GPU clusters for efficient training, finetuning, and inference of AI models, ensuring optimal performance and scalability.

  • Explore and implement efficient inference techniques to reduce computational costs and improve the speed of model predictions without compromising quality.

  • Stay ahead of the curve in AI research, particularly in the areas of LLMs, to continually improve our technologies and methodologies.

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