At Marvell, we believe that infrastructure powers progress. That execution is as essential as innovation. That better collaboration builds better technology. Trusted by the world’s leading technology companies for 25 years, we move, store, process and secure the world’s data with semiconductor solutions designed for our customers’ current needs and future ambitions. Through a process of deep collaboration and transparency, we’re ultimately changing the way tomorrow’s enterprise, cloud, automotive, and carrier architectures transform—for the better.
The data infrastructure that our customers build has never been more critical to our global economy. It’s what’s keeping the world connected, businesses running, and information flowing. If you’re ready to excel, innovate, and truly enjoy your work, apply now for the position detailed below.
Machine Learning(ML) IP Development is part of the SW Engineering Team, in the Processor Business (IPBU) group at Marvell. This group is responsible for developing ML/DL IP for Automotive use cases.
The candidate will be able to choose from the following tasks:
- Profiling, Quantizing and characterizing new DL models for Automotive
- Writing/extending a timing approximate simulator for a scalable DL accelerator in SystemC
- Candidate must be a graduate student and pursuing a MS degree(or higher) in EE/CS with a BS in CS or EE.
- Must have basic understanding of Deep Learning Models (PyTorch preferred)
- Must have a good understanding of computer architecture
- Experience with running/training models on GPU’s is a plus.
- If selecting to write a simulator (see below), should have good SW engineering/coding skills in C/C++
Marvell provides a work environment that promotes employee growth and development. We are searching for an individual who wants to grow with the company and will strive to improve performance. If you are driven, personable, and energetic, there will be additional opportunities for you here at Marvell.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status.