Deep Learning Compiler Engineer
Quadric
Deep Learning Compiler Engineer
Quadric
GPNPU hardware and software for ML
Quadric 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.
Quadric 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.
Deep 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.
Within 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.
Required
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.
Preferred
Experience with low-level kernel optimization, SIMD/vector programming, and memory allocation strategy development.
Knowledge of model quantization methodologies (INT8, FP8, mixed-precision) and post-training/QAT optimization techniques.
Prior experience working on software stacks for custom AI accelerators, DSPs, or embedded architectures.
Experience with FPGA bring-up, hardware emulation, or cycle-accurate simulator development.
Quadric also offer a variety of benefits to support your needs. The benefits below reflect our India-based offerings; for roles in other locations, benefits vary and are shared during the hiring process. These include:
Medical, dental, and vision insurance
Equity with the business
Paid Parental Leave
PF Retirement Plan
Flexible PTO
Winter holiday shutdown
Catered lunch each day in our office
Downtown Pune office location, close to shops, cafes, and local amenities
Collaborative, low-ego culture with significant ownership and impact
A work culture focused on innovative disruption
If this role resonates with you, we encourage you to apply even if your experience does not perfectly match every qualification. We value potential, curiosity, and a willingness to learn just as much as direct experience.
Quadric is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, marital status, medical condition, or any other characteristic protected by applicable federal, state, or local law
Quadric participates in the E-Verify program to confirm employment eligibility for U.S.-based roles. As part of this process, applicants may review the following notices, which explain your rights and our participation in E-Verify. These notices are provided in English and Spanish.
No action is required from candidates during the application process. These notices are provided for informational purposes only.
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Quadric.io provides an integrated hardware-software platform for ML on System-on-Chips, centered on its Chimera General-Purpose Neural Processing Unit (GPNPU). Developers write ML code in C, which is compiled by the Chimera LLVM C Compiler, simulated with the Chimera Instruction Set Simulator (ISS), and deployed via a Software Development Kit (SDK) that supports design, profiling, and execution on Chimera. The solution is a single, end-to-end pipeline that eliminates the need to partition code across DSPs/GPUs and CPUs, offering tightly integrated hardware and software tools that can run a wide range of C-based neural-network operations. Quadric.io aims to simplify and accelerate the design of ML-enabled SoCs for semiconductor developers and manufacturers, enabling faster time-to-market and better ML performance through its cohesive hardware-software stack.
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