Compiler Engineer II - Machine Learning, Annapurna Labs
: Job Details :

The AWS Neuron Compiler team is actively seeking skilled compiler engineers to join our efforts in developing a state-of-the-art deep learning compiler stack. This stack is designed to optimize application models across diverse domains, including Large Language and Vision, originating from leading frameworks such as PyTorch, TensorFlow, and JAX. Your role will involve working closely with our custom-built Machine Learning accelerators, including Inferentia/Trainium, which represent the forefront of AWS innovation for advanced ML capabilities, powering solutions like Generative AI.

In this role as a ML Compiler engineer, you'll be instrumental in designing, developing, and optimizing features for our compiler. Your responsibilities will involve tackling crucial challenges alongside a talented engineering team, contributing to leading-edge design and research in compiler technology and deep-learning systems software. Additionally, you'll collaborate closely with cross-functional team members from the Runtime, Frameworks, and Hardware teams to ensure system-wide performance optimization.

As part of the Backend team, you'll play a significant role in designing and developing various aspects of our system. This includes but is not limited to instruction scheduling, memory allocation, data transfer optimization, graph partitioning, parallel programming, code generation, Instruction Set Architectures, new hardware bring-up, and hardware-software co-design.

Key job responsibilities include:

A day in the life:

Minimum qualifications:

• B.S. or M.S. in computer science or related field.
• Proficiency with 1 or more of the following programming languages: C++ (preferred), Python.
• 3+ years of non-internship professional software development experience.
• 2+ years of experience developing compiler optimization, graph-theory, hardware bring-up, FPGA placement and routing algorithms, or hardware resource management.

Preferred qualifications:

• M.S. or Ph.D. in computer science or related field.
• Strong knowledge in one or more of the areas of: compiler design, instruction scheduling, memory allocation, data transfer optimization, graph partitioning, parallel programming, code generation, Instruction Set Architectures, new hardware bring-up, and hardware-software co-design.
• Experience with LLVM and/or MLIR.
• Experience with developing algorithms for simulation tools.
• Experience in TensorFlow, PyTorch, and/or JAX.
• Experience in LLM, Vision or other deep-learning models.

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