Principal AI Compiler Engineer

Sptix; India; On-site; INR 3,000,000 - 7,000,000; Full time

Job summary

EnCharge AI is seeking a highly skilled AI Compiler Engineer to spearhead the development and optimization of graph compilers for cutting-edge AI and ML workloads. You will collaborate with hardware architects and AI researchers to enhance performance and enable efficient model deployment on EnCharge’s Inference Accelerators.

Responsibilities include designing optimizations for neural network execution on graphs, implementing IR generation, and leading a team of engineers in compiler passes and

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field (Ph.D. preferred).
• 10–15 years of experience in compiler development with a focus on AI/ML graph compilers.
• Proficiency in AI graph compiler frameworks (e.g., MLIR, Torch-FX).

Responsibilities

• Spearhead development and optimization of graph compilers for AI/ML workloads.
• Collaborate with hardware architects and AI researchers to deploy models on accelerators.
• Architect and implement optimizations for AI model execution to improve performance and latency.
• Lead and mentor a team of engineers focused on graph compiler optimizations.
• Research and integrate advancements in compiler design and hardware acceleration.

Skills

Graph compiler development

MLIR

Torch-FX

Hardware optimization

Team mentorship

Education

Bachelor's/Master's in CS/EE (PhD preferred)

Tools

MLIR

Torch-FX

Job description

EnCharge AI is seeking a highly skilled and experienced AI Compiler Engineer to spearhead the development and optimization of graph compilers tailored to cutting-edge AI and ML workloads. This role involves collaboration with hardware architects and AI researchers to enhance performance and enable efficient model deployment on EnCharge’s Inference Accelerators.

Responsibilities

• spearhead the development and optimization of graph compilers tailored to cutting-edge AI and ML workloads.
• collaborate with hardware architects and AI researchers to enhance performance and enable efficient model deployment on EnCharge’s Inference Accelerators.
• architect, design, and implement optimizations for AI model execution on graph compilers to improve performance, reduce latency, and maximize hardware utilization.
• collaborate with ML researchers, hardware engineers, and software developers to design and deploy AI models, addressing hardware‑specific challenges.
• work on performance optimizations for neural network models, including 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.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field (Ph.D. preferred).
• 10-15 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.
• Strong understanding of intermediate representations, code parsing, and semantic analysis in compiler design.