ML Compiler Engineer for AI Accelerators

Amazon; Cupertino (CA); On-site; USD 165,000 - 224,000; Full time

Job summary

Amazon’s AWS Neuron Compiler team in Cupertino, CA is seeking a skilled ML compiler engineer to design, develop, and optimize a state-of-the-art compiler stack for deep learning models on Inferentia/Trainium accelerators.

You will collaborate with Runtime, Frameworks, and Hardware teams to deliver performance improvements, contribute to research, and build features from scheduling to code generation.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field.
• 3+ years of non-internship professional software development experience
• Proficiency with C++
• Experience with optimization algorithms, graph-theory, hardware bring-up, FPGA placement and routing algorithms, or hardware resource management

Responsibilities

• Solve challenging technical problems, often ones not solved before, at every layer of the stack.
• Design, implement, test, deploy and maintain innovative software solutions to transform service performance, durability, cost, and security.
• Research implementations that deliver the best possible experiences for customers.
• Build high-impact solutions to deliver to our large customer base.
• Participate in design discussions, code review, and communicate with internal and external stakeholders.
• Work cross-functionally to help drive business decisions with your technical input.

Skills

C++ programming

Optimization algorithms

Graph theory

Hardware bring-up

FPGA placement & routing

Memory management

Code generation

Parallel programming

DL frameworks

DL models

Education

Bachelor's degree in CS/Engineering/Math

Tools

LLVM

MLIR

Job description

Amazon’s AWS Neuron Compiler team in Cupertino, CA is seeking a skilled ML compiler engineer to design, develop, and optimize a state-of-the-art compiler stack for deep learning models on Inferentia/Trainium accelerators.

You will collaborate with Runtime, Frameworks, and Hardware teams to deliver performance improvements, contribute to research, and build features from scheduling to code generation.