Machine Learning Engineer
- Published on
About the Role
Join our innovative team as a Machine Learning Engineer, where we are at the forefront of revolutionizing hardware engineering with advanced models designed to enhance the design process. Your role will involve developing and evaluating complex systems comprised of interconnected LLMs, NLP models, and retrieval algorithms. By structuring data for training, optimizing models under various constraints, and designing innovative search algorithms, you will significantly contribute to our mission of autonomously generating cutting-edge hardware designs.
About the Candidate (Expectations and Nice to Have Skills)
The candidate should have a strong understanding of NLP and LLMs, and proficiency in PyTorch. They should demonstrate exceptional data engineering and AI capabilities, including publications in top AI conferences, and experience in leading AI companies and research labs. A passion for solving challenging problems, especially in complex ML domains, is crucial.
About the Company
We are dedicated to transforming hardware design processes, founded by leaders with backgrounds from top-tier institutions and supported by renowned investors. Our mission is to streamline hardware design by leveraging expertise from the intersecting fields of AI and semiconductors.
Company Culture and Benefits
- Opportunity to pioneer advancements in hardware engineering through AI integration
- Collaborative environment emphasizing innovation and cutting-edge technology
- Competitive salary and comprehensive benefits package
- Flexible working hours with potential for remote work
- Professional growth opportunities; continuous learning support
Key Responsibilities
- Develop and evaluate extensive systems consisting of interconnected LLMs, NLP models, and retrieval algorithms
- Structure data appropriately for training complex models
- Build and optimize both open-source and closed-source models under various constraints
- Design and execute innovative search and retrieval algorithms
- Implement and evaluate models while addressing engineering challenges such as latency and inference costs.