Staff AI Compiler Engineer

EnCharge AI; India; On-site; INR 4,000,000 - 9,000,000; Full time

Job summary

EnCharge AI seeks a senior compiler engineer to architect, design, and optimize AI model execution on graph compilers, collaborating with ML researchers, hardware engineers, and software developers to deploy scalable models across GPUs, TPUs, and ASICs.

You will lead efforts on fusion, quantization, tiling, and code generation for neural networks, implement IR generation, and mentor a team delivering high-impact compiler optimizations.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field (Ph.D. preferred).
• 10-20 years in compiler development with a focus on AI/ML graph compilers.
• Proficiency in AI graph compiler frameworks (e.g., MLIR, Torch-FX).
• Solid background in hardware architectures (GPUs, TPUs, ASICs) and optimization techniques (fusion, quantization, tiling).
• Familiarity with neural networks operators and code generation.
• Strong understanding of IR, parsing, and semantic analysis in compiler design.
• Proficiency in C++, Python, or other compiler-relevant languages.
• Open-source contributions to AI software frameworks are a plus.
• Demonstrated experience leading and mentoring engineering teams with successful project delivery.

Responsibilities

• Architect, design, and implement optimizations for AI model execution on graph compilers to improve performance, reduce latency, and maximize hardware utilization.
• Work closely with ML researchers, hardware engineers, and software developers to design and deploy AI models, understanding and addressing hardware-specific challenges.
• Work on performance optimizations for neural network models, such as layer fusion, operator fusion, and graph-level transformations.
• Develop compiler optimizations and passes that convert high-level AI models into intermediate representations (IR).
• Implement parsing, semantic analysis, and IR generation for deep learning frameworks.
• Research and integrate the latest advancements in compiler design, ML model optimizations, and hardware acceleration into graph compilers.
• Provide leadership, mentorship, and technical guidance to a team of engineers focused on graph compiler optimizations.

Skills

AI graph compiler

C++, Python

Leadership & mentorship

NN operators & code generation

IR, parsing, semantic analysis

Fusion, quantization, tiling

Open-source contributions

GPU/TPU/ASIC architecture

Education

Bachelor’s/Master’s in CS/EE

Tools

MLIR

Torch-FX

Job description

The 10 Hottest Semiconductor Startups Of 2025 (So Far)

Responsibilities

• Architect, design, and implement optimizations for AI model execution on graph compilers to improve performance, reduce latency, and maximize hardware utilization.
• Work closely with ML researchers, hardware engineers, and software developers to design and deploy AI models, understanding and addressing hardware-specific challenges.
• Work on performance optimizations for neural network models, such as layer fusion, operator fusion, and graph-level transformations.
• Develop compiler optimizations and passes that convert high-level AI models (e.g., from TensorFlow, PyTorch) into intermediate representations (IR).
• Implement parsing, semantic analysis, and IR generation for deep learning frameworks.
• Research and integrate the latest advancements in compiler design, ML model optimizations, and hardware acceleration into graph compilers.
• Provide leadership, mentorship, and technical guidance to a team of engineers focused on graph compiler optimizations.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field (Ph.D. preferred).
• 10-20 years in compiler development, with a strong focus on AI or ML graph compilers.
• Proficiency in AI graph compiler frameworks (e.g., MLIR, Torch-FX)
• Solid background in hardware architectures (e.g., GPUs, TPUs, ASICs) and optimization techniques such as fusion, quantization, and tiling.
• Familiarity with neural networks operators and code generation.
• Strong understanding of intermediate representations, code parsing, and semantic analysis in compiler design.
• Proficiency in C++, Python, or other programming languages commonly used in compiler development.
• Open-source contributions to AI software frameworks and libraries is a plus
• Demonstrated experience leading and mentoring engineering teams with successful project delivery