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Lead Software Engineer, ML (EDA)

114 800 - 213 200$
Тип работы
fulltime
Грейд
lead
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
Lead Software Engineer, ML (EDA): Building machine learning and agentic AI capabilities for Electronic Design Automation tools with an accent on optimization algorithms, deep learning, APIs, and production-grade software. Focus on translating research into scalable software, integrating ML models and agentic workflows, and solving complex digital design and signoff challenges.

Location: San Jose, California, United States

Annual salary: $114,800–$213,200 in California, plus potential bonus, equity, and benefits.

Company

hirify.global develops Electronic Design Automation tools for digital design and signoff workflows.

What you will do

  • Design and implement gradient-based and gradient-free optimization algorithms for complex EDA and system design challenges.
  • Research, prototype, and develop machine learning and deep learning approaches for EDA tools.
  • Design, implement, verify, and maintain production-quality software for digital design and signoff.
  • Build APIs and software components that integrate ML models, agentic AI workflows, and optimization techniques into production tools.
  • Translate deep learning research concepts into scalable, maintainable software solutions.
  • Collaborate with research, engineering, product, and customer-facing organizations across worldwide teams.

Requirements

  • Strong software engineering experience developing, verifying, and maintaining production-quality software.
  • Proficiency in Python and C/C++.
  • Experience with Linux-based development environments.
  • Solid understanding of machine learning and deep learning algorithms, with hands-on experience using frameworks such as PyTorch.
  • Experience or strong interest in agentic AI, LLM-based workflows, and agentic frameworks.
  • Ability to design APIs and communicate effectively in a global, cross-functional environment.

Nice to have

  • Understanding of VLSI design, EDA flows, or digital implementation and signoff concepts.
  • Experience with design space exploration, optimization methods, or performance-driven engineering workflows.
  • Knowledge of probability theory and statistical modeling.
  • Experience applying machine learning to chip design, system design, or other complex engineering domains.
  • Ability to evaluate deep learning research and apply it to practical software development.

Culture & Benefits

  • Collaborative environment connecting research-oriented discussions with hands-on software delivery.
  • Paid vacation and paid holidays.
  • 401(k) plan with employer match.
  • Employee stock purchase plan.
  • Medical, dental, and vision plan options.

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