- How to use LLMs to improve hardware engineer efficiency?
- How to build a foundation model for EDA applications?
- How to architect generative AI solutions across a range of hardware design applications?
- Architect Generative AI and Deep Learning applications for hardware design problems.
- Conduct experiments to evaluate model performance, identify areas for improvement, and implement optimizations.
- Collaborate with cross-functional teams to design and develop scalable solutions that meet business objectives.
- Participate in generative AI platform team to align with application requirements, deployment models and release timelines.
- Communicate complex technical concepts and findings to both technical and non-technical stakeholders.
- Lead solution architecture reviews, platform alignment and deployment models for generative AI applications.
- Drive innovation in new generative AI approaches and keep up to date on latest research in the field.
- 1+ years of experience in applying machine learning and AI methods to engineering problems.
- This role requires a minimum of 2 years of experience working in hardware chip design or working in EDA/Cad tool development.
- Familiarity with python, and background in data structures and algorithms.
- Experience with LLMs, GPT models and other Generative AI techniques.
- Strong problem-solving skills and ability to work independently as well as collaboratively in a team environment.
- Excellent communication and presentation skills, with the ability to communicate complex technical concepts to both technical and non-technical stakeholders.
- Good understanding with hands-on experience in data cleansing and modeling for deep learning models in at least one domain (language, image, graphs, etc.)
- Experience with cloud-based machine learning platforms such as AWS, GCP, or Azure
- MS or PhD in Engineering/Science with 10-12 years' experience in computer science, Electrical Engineering, or related field.
- 2+ years of experience in applying machine learning and AI methods to engineering problems in hardware chip design or architecture.
- Strong understanding with hardware chip design and EDA methodologies, with demonstrable mastery of some aspect of the hardware design space.
- Understanding of deep learning architectures like Recurrent Neural Networks (RNNs), Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs).
- Experience with deep learning frameworks like TensorFlow or PyTorch.
- Broad expertise and understanding of AI, NLP, LLM, and generative AI trends.
- Experience prototyping, experimenting, and testing with large datasets and training models.
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Solutions Engineering, Principal Engineer - Sunnyvale, CA, United States - Synopsys
Description
Machine Learning Solutions Engineer, Sr. Staff
45900BR
USA - California - Mountain View/Sunnyvale, USA - North Carolina - Durham, USA - Texas - Austin
Job Description and Requirements
We are seeking a highly skilled and pioneering Generative AI Solutions Engineer to join our Gen-AI Center of Excellence (COE). As a Solutions Engineer, you will be responsible for innovating and contributing to the advancement of artificial intelligence in EDA. This work will greatly influence Synopsys' customers with questions, like the following:
#LI-PG1
Job Category
Engineering
Country
United States
Job Subcategory
Solutions Engineering
Hire Type
Employee
Base Salary Range
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