Job Details
Amazon Web Services (AWS) internships are full-time (40 hours/week) for 12 consecutive weeks during summer. By applying to this position, your application will be considered for all locations we hire for in the United States.
In Annapurna Labs we are at the forefront of hardware co-design not just in Amazon Web Services (AWS) but across the industry. The work we do is cutting-edge and internet-scale while also being deeply important to our customers. We design and build every component of our hardware and software to come together into products that our customers use for accelerated computing: either Machine Learning acceleration, or FPGA acceleration. We get our hands dirty, from creating our own silicon, pushing the electrons in the right direction, ensuring our hardware is functional and healthy, and managing the full lifecycle of our systems at the huge scale and complexity of AWS. If you're interested in "building a complete product" from inception to delighted customers, Annapurna is a fantastic choice.
As a member of the Cloud-Scale Machine Learning Acceleration team you ll be responsible for the design and optimization of Software and Hardware in our data centers including technologies such as AWS Inferentia, which is a machine learning inference accelerator designed to deliver high performance at low cost.
If this sounds exciting to you - come build the future with us!
Key job responsibilities
Perform physical design for Amazon's machine learning custom silicon solutions
Participate in various aspects of physical design: full chip floorplanning, circuit analysis, power/clock distribution, timing optimization, place and route, power integrity analysis, and physical verification
Write Tcl or PERL scripts to improve physical design flows and methods
Collaborate with RTL, DFT designers to ensure high quality design implementation
We are open to hiring candidates to work out of one of the following locations:
Austin, TX, USA | Cupertino, CA, USA
BASIC QUALIFICATIONS
Enrolled in a Bachelors degree program or higher in Electrical Engineering, Computer Engineering, or a related field with a graduation conferral date between December 2024 and September 2025
Scripting internship/project experience with Python, Perl or equivalent
Strong understanding of VLSI circuit design fundamentals
Have taken at least one or more VLSI circuit design and implementation classes and done lab projects
PREFERRED QUALIFICATIONS
Enrolled in a Master s degree program in Electrical Engineering or Computer Engineering
Previous relevant technical internship experience
Experience with FinFET design, Clock/Power Distribution, Spice Circuit analysis
Experience with Place and Route, digital implementation
Experience with EDA tools from Synopsys (ICC2, DC, PT), Cadence (Genus, Innovus, Voltus)
Experience with Verilog/SystemVerilog
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $104,000/year in our lowest geographic market up to $212,200/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit Applicants should apply via our internal or external career site.
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Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
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