обновлено 2 дня назад
Lead Software Engineer, ML (EDA)
114 800 - 213 200$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
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
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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