AI/ML Research Scientist

Posted Yesterday
Be an Early Applicant
Hiring Remotely in Armenia
Remote
Junior
Information Technology • Software
The Role
Conduct research applying advanced ML (GNNs, diffusion models, transformers) to semiconductor design optimization. Implement and prototype algorithms in Python/C++, validate approaches, publish in top ML venues, and collaborate with engineers to integrate solutions into EDA tools.
Summary Generated by Built In
Description

We are looking for a AI/ML Research Scientist to join our team. In this role, you will drive innovative research at the intersection of artificial intelligence and semiconductor design, developing novel machine learning approaches to solve complex optimization challenges. You will work closely with world-class engineers and researchers to transform cutting-edge ideas into practical solutions that improve chip performance, efficiency, and scalability.

Responsibilities:

  • Conduct cutting-edge research in applying machine learning techniques to chip design and optimization problems
  • Develop and implement algorithms using graph neural networks, diffusion models, and other advanced ML architectures
  • Collaborate with cross-functional teams including engineers and researchers
  • Publish research findings in top-tier conferences and journals
  • Prototype and validate new approaches using modern ML frameworks
  • Contribute to open-source or commercial EDA tools
Requirements

Must have skills:

  • PhD with 1+ years of research experience in Electrical Engineering, Computer Science, Information Systems, or STEM-related field
  • 1+ years of experience with modern machine learning architectures including Transformers, Graph Neural Networks, and Diffusion models
  • Strong coding skills in Python and C++
  • Experience with machine learning frameworks like PyTorch
  • At least 1 publication in top machine learning conferences (NeurIPS, ICML, ICLR, etc.)

Nice to have:

  • Experience with deep learning on graphs (general graphs, circuit graphs, molecular graphs, or trees)
  • Familiarity with classical search and optimization techniques (Genetic Algorithms, Monte-Carlo Tree Search, Dijkstra's Algorithm, etc.)
  • Experience with EDA tools, either commercial or open-source
  • Publications in top-tier venues focusing on AI for chip design or AI for materials science

Skills Required

  • PhD with 1+ years of research experience in Electrical Engineering, Computer Science, Information Systems, or STEM-related field
  • 1+ years of experience with modern machine learning architectures including Transformers, Graph Neural Networks, and Diffusion models
  • Strong coding skills in Python and C++
  • Experience with machine learning frameworks like PyTorch
  • At least 1 publication in top machine learning conferences (NeurIPS, ICML, ICLR, etc.)
  • Experience with deep learning on graphs (general graphs, circuit graphs, molecular graphs, or trees)
  • Familiarity with classical search and optimization techniques (Genetic Algorithms, Monte-Carlo Tree Search, Dijkstra's Algorithm, etc.)
  • Experience with EDA tools, either commercial or open-source
  • Publications in top-tier venues focusing on AI for chip design or AI for materials science
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The Company
HQ: Petaling Jaya
399 Employees
Year Founded: 2005

What We Do

Commit is a global tech services company with offices in Israel, US, Canada, UK, and Europe. The company was founded in 2005 and has over 700 multi-disciplinary innovation experts who serve a broad range of companies, from small startups to large enterprises in multiple business sectors. Commit specializes in advanced technologies and applications with dedicated practices in Cloud, GenAI, Software, IoT, Big Data, Cyber, Collaboration, Data center migration projects, and more. Commit offers innovative, end-to-end technology solutions by developing custom software and IoT platforms for clients looking to build their next-gen products within the modern ICT world. Commit’s complete and comprehensive engineering powerhouse of resources, and proprietary Flexible R&D methodology helps transform its clients’ technology visions into high-quality products while reducing costs and improving time-to-market.

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