**Job Description**
As a member of the Cloud-Scale Machine Learning Acceleration team at Amazon, you will be responsible for the design and optimization of software and hardware in Amazon's data centers, specifically focusing on technologies such as AWS Inferentia. This machine learning inference accelerator is dedicated to delivering high performance at low cost. Your duties will include performing physical design for Amazon's custom silicon solutions, which encompasses full chip floorplanning, circuit analysis, timing optimization, and more. Additional responsibilities involve writing Tcl or PERL scripts to enhance design flows and collaborating with designers to ensure quality implementation.
• *Requirements and Qualifications**
- Enrolled in a Bachelor's degree program or higher in Electrical Engineering, Computer Engineering, or a related field (graduation between December 2025 and September 2026)
- Scripting experience with Python, Perl, or equivalent
- Strong understanding of VLSI circuit design fundamentals
- Completed VLSI circuit design and implementation coursework with lab projects
• *Preferred Qualifications**
- Enrollment in a Master's degree program in Electrical or Computer Engineering
- Relevant technical internship experience
- Experience in FinFET design, clock/power distribution, and Spice Circuit analysis
- Familiarity with place and route processes and digital implementation
- Experience with EDA tools from Synopsys, Cadence
- Experience with Verilog/SystemVerilog
• *Pay and Benefits**
The base salary for this position ranges from $104,000 to $212,200 annually, depending on geographic location.
• *Company Information**
Amazon is a global technology company headquartered in Seattle, Washington. Originally founded as an online bookstore, Amazon has grown to become a dominant force in e-commerce, cloud computing through AWS, digital streaming, and artificial intelligence. The company is known for its customer-centric approach, innovative technologies, and a commitment to diversity and inclusion in the workplace.