ML Accelerator Scientist — Pre-Silicon Verification

Amazon Inc.; Cupertino (CA); Hybrid; USD 172,000 - 222,000; Full time

Job summary

Amazon's Annapurna Labs in Cupertino is seeking an Applied Scientist specialized in machine learning acceleration. You will contribute to pre-silicon verification of a custom ML chip by building content generation frameworks, testing strategies, and solving complex scheduling problems.

The role requires modeling ML instruction behavior, developing APIs, deploying concurrent algorithms, and creating coverage metrics; collaboration with hardware engineers is essential.

Qualifications

• 3+ years of building models for business application experience.
• PhD or Master's with 4+ years in CS, CE, ML or related field.
• Experience in patents or publications at top-tier conferences or journals.
• Experience programming in Java, C++, Python or related language.
• Experience in algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing.
• Experience using Unix/Linux.
• Experience in professional software development.

Responsibilities

• Develop an understanding for a custom machine learning instruction set architecture.
• Model correctness of instruction streams using first order logic.
• Create custom API's to allow control over scheduling and randomness.
• Deploy algorithms to ensure concurrent code is safely constructed.
• Create coverage metrics to ensure solution space coverage.
• Use novel methods like machine learning to automate content creation.

Skills

Java

C++

Python

Unix/Linux

Software development

Education

PhD or Master's with 4+ years in CS/CE/ML

Tools

Unix/Linux

Job description

Amazon's Annapurna Labs in Cupertino is seeking an Applied Scientist specialized in machine learning acceleration. You will contribute to pre-silicon verification of a custom ML chip by building content generation frameworks, testing strategies, and solving complex scheduling problems.

The role requires modeling ML instruction behavior, developing APIs, deploying concurrent algorithms, and creating coverage metrics; collaboration with hardware engineers is essential.