Senior/Staff Deep Learning Compiler Engineer

We Love AI Jobs; Pune District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Quadric is seeking a senior/staff Deep Learning Compiler Engineer to design and optimize the compiler stack (MLIR/TVM/LLVM) that translates neural network models to Quadric’s edge hardware. You’ll implement optimizations, graph transformations, and performance profiling to unlock peak edge AI performance.

The role involves close collaboration with hardware teams and working on transformer/CNN models, quantization, and memory management to ensure low latency and high efficiency on our edge

Qualifications

• 8+ years of experience in deep learning compilers or compiler infrastructures.
• Hands-on experience with MLIR, TVM, LLVM, XLA, TensorRT, or Glow.
• Strong proficiency in C++ (14/17/20) and Python; solid data structures & OOP knowledge.

Responsibilities

• Design, implement, and maintain compiler optimization passes for Quadric’s processor architecture.
• Develop lowering paths from high-level frameworks (PyTorch, TensorFlow, ONNX) to optimized kernel code.
• Implement graph-level optimizations: operator fusion, quantization, memory allocation strategies.
• Collaborate with hardware teams to align compiler features with architectural needs.

Skills

C++

Python

MLIR

TVM

LLVM

PyTorch

TensorFlow

ONNX

Education

BS, MS, or PhD in CS/CE/EE

Tools

XLA

TensorRT

Glow

Job description

# Senior/Staff Deep Learning Compiler EngineerQuadricPune, Maharashtra, INDIAFull-timeAI tools:MLIRApache TVMLLVMPyTorchTensorFlowONNXXLATensorRTUpload your resume once and Scout applies for youApply with ScoutShare this job:LinkedInXSave imageThis senior/staff engineer will develop and optimize the deep-learning compiler stack that connects neural network frameworks to Quadric’s edge processor. The role covers compiler passes, model graph transformations, performance analysis, and collaboration with hardware teams.* Permanent* On-site* 8+ years* BS, MS, or PhD### Skills and expertiseC++PythonMLIRApache TVMLLVMPyTorchTensorFlowONNX### Key responsibilities* Develop compiler optimization passes and lowering pathways for Quadric’s processor architecture.* Implement graph transformations, including operator fusion, quantization, and memory allocation strategies.* Profile model performance and collaborate with hardware teams on compiler and architecture requirements.## How this job comparesCompared with 94 open staff machine learning jobs on October 10, 2026.Similar open jobs: We Love AI Jobs lists 94 open staff machine learning jobs at 53 companies.Remote work: This job is on site. 16 of the 94 staff machine learning jobs (17%) are remote.AI in this role: High. AI is the core of the work: building, training or deploying AI systems. The role builds the compiler stack used to run neural networks on Quadric hardware. It directly handles model graphs, operators, quantization, and end-to-end AI performance.## Full descriptionQuadric is redefining edge AI with the industry's first General Purpose Neural Processing Unit (GPNPU), enabling developers to run both neural network inference and conventional C++ code on a single programmable architecture. Our technology powers intelligent edge devices across automotive, industrial, robotics, and embedded systems.Founded in 2016 and based in downtown Burlingame, California, Quadric is building the world's first supercomputer designed for the real-time needs of edge devices. Quadric aims to empower developers in every industry with superpowers to create tomorrow's technology, today. The company was co-founded by technologists from MIT and Carnegie Mellon, who were previously the technical co-founders of the Bitcoin computing company 21.The OpportunityQuadric is building the world’s first General-Purpose Neural Processing Unit (GPNPU) architecture, bringing high-performance AI, DSP, and ML compute to edge devices. As a Deep Learning Compiler Engineer, you will design and optimize the compiler stack (MLIR, TVM, LLVM) that bridges state-of-the-art neural network frameworks directly to our proprietary hardware architecture. You'll play a critical role in unlocking peak hardware performance, low latency, and memory efficiency for edge AI workloads.What You'll DoDeep Learning Compiler Infrastructure & Optimization• Design, implement, and maintain compiler optimization passes targeting Quadric’s processor architecture using frameworks like MLIR, Apache TVM, or LLVM.• Develop lowering pathways from high-level machine learning frameworks (PyTorch, TensorFlow, ONNX) down to optimized low-level kernel code.• Optimize neural network performance for memory throughput, latency, compute unit utilization, and power consumption.Neural Network Model Parsing & Graph Transformation• Implement graph-level optimizations, including operator fusion, layout transformation, quantization (INT8/FP16), and memory allocation strategies.• Analyze novel deep learning model topologies (Transformers, CNNs, Vision-Language Models) and extend compiler support for new operators and primitives.• Benchmark and profile end-to-end model performance to identify and resolve compiler bottlenecks.Hardware-Software Co-Design• Collaborate closely with hardware and micro-architecture teams to define instruction set extensions, hardware acceleration features, and compiler requirements.• Develop software simulators, functional models, and test benches to validate compiler correctness and generated binary performance.• Participate in hardware bring-up and validation efforts on FPGA and ASIC platforms.What Success Looks LikeWithin your first 6–12 months, you'll:• Successfully integrate support for key deep learning models (e.g., modern Transformer architectures or Vision models) into Quadric's compiler pipeline.• Implement custom graph and codegen optimization passes that deliver measurable performance improvements on target benchmarks.• Partner with the architecture team to influence the next-generation micro-architecture definition through data-driven workload analysis.What We're Looking ForRequired• 8 years+ experience alongwith BS, MS, or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or a related field.• Hands-on experience developing deep learning compilers or compiler infrastructures (e.g., MLIR, Apache TVM, LLVM, XLA, TensorRT, or Glow).• Strong proficiency in C++ (14/17/20) and Python, with solid fundamentals in data structures, algorithms, and object-oriented design.• Familiarity with modern AI/ML frameworks (PyTorch, TensorFlow, ONNX) and deep learning operator representations.• Solid understanding of computer systems, memory hierarchies, parallel processing, and CPU/GPU/NPU instruction execution.