6 Tvm Jobs

Interested in accelerating machine learning and artificial intelligence on mobile devices for millions of users Come join our team. We are building software platforms that enable users of Qualcomms silicon to construct optimized neural networks and machine learning algorithms. We are looking for software engineers with a machine learning or compiler background who will help us build these software platforms. Required Skills & Experience Programming in C/C++ . 0 to 10 years of software engineering or related work experience . Preferred Qualifications Experience in machine learning frameworks such as MxNet/NNVM/TVM, PyTorch, TensorFlow, Caffe. OR experience in compilers with an interest in machine learning. Deep knowledge of software engineering . Minimum Qualifications Bachelor's degree in Engineering, Information Systems, Computer Science, or related field. Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field. 2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc.

Interested in accelerating machine learning and artificial intelligence on mobile devices for millions of users Come join our team. We are building software platforms that enable users of Qualcomms silicon to construct optimized neural networks and machine learning algorithms. We are looking for software engineers with a machine learning or compiler background who will help us build these software platforms. Key Responsibilities: In this role, you will construct and tune machine learning frameworks, build compilers and tools, and collaborate with Qualcomm hardware and software engineers to enable efficient usage of Qualcomms silicon for machine learning applications. Minimum Qualifications: Bachelors degree in Engineering, Information Systems, Computer Science, or related field. Programming in C/C++ 2 to 4 years of software engineering or related work experience Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field. 2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc. Preferred Qualifications: Experience in machine learning frameworks such as MxNet/NNVM/TVM, Pytorch, Tensorflow, Caffe OR experience in compilers with an interest in machine learning Deep knowledge of software engineering

We are looking for a Principal AI/ML Engineer with expertise in model inference, optimization, debugging, and hardware acceleration . This role will focus on building efficient AI inference systems, debugging deep learning models, optimizing AI workloads for low latency, and accelerating deployment across diverse hardware platforms. In addition to hands-on engineering, this role involves cutting-edge research in efficient deep learning, model compression, quantization, and AI hardware-aware optimization techniques . You will explore and implement state-of-the-art AI acceleration methods while collaborating with researchers, industry experts, and open-source communities to push the boundaries of AI performance. This is an exciting opportunity for someone passionate about both applied AI development and AI research , with a strong focus on real-world deployment, model interpretability, and high-performance inference. Education & Experience: 20+ years of experience in AI/ML development, with at least 5 years in model inference, optimization, debugging, and Python-based AI deployment. Masters or Ph.D. in Computer Science, Machine Learning, AI. Leadership & Collaboration: Lead a team of AI engineers in Python-based AI inference development. Collaborate with ML researchers, software engineers, and DevOps teams to deploy optimized AI solutions. Define and enforce best practices for debugging and optimizing AI models. Key Responsibilities: Model Optimization & Quantization: Optimize deep learning models using quantization (INT8, INT4, mixed precision etc), pruning, and knowledge distillation. Implement Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) for deployment. Familiarity with TensorRT, ONNX Runtime, OpenVINO, TVM. AI Hardware Acceleration & Deployment: Optimize AI workloads for Qualcomm Hexagon DSP, GPUs (CUDA, Tensor Cores), TPUs, NPUs, FPGAs, Habana Gaudi, Apple Neural Engine. Leverage Python APIs for hardware-specific acceleration, including cuDNN, XLA, MLIR. Benchmark models on AI hardware architectures and debug performance issues. AI Research & Innovation: Conduct state-of-the-art research on AI inference efficiency, model compression, low-bit precision, sparse computing, and algorithmic acceleration. Explore new deep learning architectures (Sparse Transformers, Mixture of Experts, Flash Attention) for better inference performance. Contribute to open-source AI projects and publish findings in top-tier ML conferences (NeurIPS, ICML, CVPR). Collaborate with hardware vendors and AI research teams to optimize deep learning models for next-gen AI accelerators. Details of Expertise: Experience optimizing LLMs, LVMs, LMMs for inference. Experience with deep learning frameworks: TensorFlow, PyTorch, JAX, ONNX. Advanced skills in model quantization, pruning, and compression. Proficiency in CUDA programming and Python GPU acceleration using cuPy, Numba, and TensorRT. Hands-on experience with ML inference runtimes (TensorRT, TVM, ONNX Runtime, OpenVINO). Experience working with RunTimes Delegates (TFLite, ONNX, Qualcomm). Strong expertise in Python programming, writing optimized and scalable AI code. Experience with debugging AI models, including examining computation graphs using Netron Viewer, TensorBoard, and ONNX Runtime Debugger. Strong debugging skills using profiling tools (PyTorch Profiler, TensorFlow Profiler, cProfile, Nsight Systems, perf, Py-Spy). Expertise in cloud-based AI inference (AWS Inferentia, Azure ML, GCP AI Platform, Habana Gaudi). Knowledge of hardware-aware optimizations (oneDNN, XLA, cuDNN, ROCm, MLIR, SparseML). Contributions to open-source community. Publications in International forums conferences journals.

General Summary: As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Systems Engineer, you will research, design, develop, simulate, and/or validate systems-level software, hardware, architecture, algorithms, and solutions that enables the development of cutting-edge technology. Qualcomm Systems Engineers collaborate across functional teams to meet and exceed system-level requirements and standards. Minimum Qualifications: Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 8+ years of Systems Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 7+ years of Systems Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field and 6+ years of Systems Engineering or related work experience. Principal Engineer Machine Learning We are looking for a Principal AI/ML Engineer with expertise in model inference , optimization , debugging , and hardware acceleration . This role will focus on building efficient AI inference systems, debugging deep learning models, optimizing AI workloads for low latency, and accelerating deployment across diverse hardware platforms. In addition to hands-on engineering, this role involves cutting-edge research in efficient deep learning, model compression, quantization, and AI hardware-aware optimization techniques . You will explore and implement state-of-the-art AI acceleration methods while collaborating with researchers, industry experts, and open-source communities to push the boundaries of AI performance. This is an exciting opportunity for someone passionate about both applied AI development and AI research , with a strong focus on real-world deployment, model interpretability, and high-performance inference . Education & Experience: 20+ years of experience in AI/ML development, with at least 5 years in model inference, optimization, debugging, and Python-based AI deployment. Masters or Ph.D. in Computer Science, Machine Learning, AI Leadership & Collaboration Lead a team of AI engineers in Python-based AI inference development . Collaborate with ML researchers, software engineers, and DevOps teams to deploy optimized AI solutions. Define and enforce best practices for debugging and optimizing AI models Key Responsibilities Model Optimization & Quantization Optimize deep learning models using quantization (INT8, INT4, mixed precision etc), pruning, and knowledge distillation . Implement Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) for deployment. Familiarity with TensorRT, ONNX Runtime, OpenVINO, TVM AI Hardware Acceleration & Deployment Optimize AI workloads for Qualcomm Hexagon DSP, GPUs (CUDA, Tensor Cores), TPUs, NPUs, FPGAs, Habana Gaudi, Apple Neural Engine . Leverage Python APIs for hardware-specific acceleration , including cuDNN, XLA, MLIR . Benchmark models on AI hardware architectures and debug performance issues AI Research & Innovation Conduct state-of-the-art research on AI inference efficiency, model compression, low-bit precision, sparse computing, and algorithmic acceleration . Explore new deep learning architectures (Sparse Transformers, Mixture of Experts, Flash Attention) for better inference performance . Contribute to open-source AI projects and publish findings in top-tier ML conferences (NeurIPS, ICML, CVPR). Collaborate with hardware vendors and AI research teams to optimize deep learning models for next-gen AI accelerators. Details of Expertise: Experience optimizing LLMs, LVMs, LMMs for inference Experience with deep learning frameworks : TensorFlow, PyTorch, JAX, ONNX. Advanced skills in model quantization, pruning, and compression . Proficiency in CUDA programming and Python GPU acceleration using cuPy, Numba, and TensorRT . Hands-on experience with ML inference runtimes (TensorRT, TVM, ONNX Runtime, OpenVINO) Experience working with RunTimes Delegates (TFLite, ONNX, Qualcomm) Strong expertise in Python programming , writing optimized and scalable AI code. Experience with debugging AI models , including examining computation graphs using Netron Viewer, TensorBoard, and ONNX Runtime Debugger . Strong debugging skills using profiling tools (PyTorch Profiler, TensorFlow Profiler, cProfile, Nsight Systems, perf, Py-Spy) . Expertise in cloud-based AI inference (AWS Inferentia, Azure ML, GCP AI Platform, Habana Gaudi). Knowledge of hardware-aware optimizations (oneDNN, XLA, cuDNN, ROCm, MLIR, SparseML). Contributions to open-source community Publications in International forums conferences journals

Compiler Lead Hyderabad Founded by highly respected Silicon Valley veterans - with its design centers established in Santa Clara, California. / Hyderabad/Bangalore A US based well-funded product-based startup looking for Highly talented Verification Engineers for the following roles. We are looking for a highly experienced systems engineer with deep expertise in compilers, machine learning infrastructure, and system-level performance optimization. This role is hands-on and research-driven, ideal for someone who thrives on solving low-level performance challenges and building core infrastructure that powers next-generation AI workloads. Key Responsibilities: Compiler Design & Optimization Develop and enhance compiler toolchains based on LLVM, MLIR, Open64, or Glow. Build and optimize intermediate representations, custom dialects, and code generation flows for AI accelerators. Implement transformations and optimizations for latency, memory usage, and compute efficiency. AI System Integration Work closely with hardware teams to co-design compilers targeting custom silicon. Integrate compiler backends with ML frameworks like PyTorch, TensorFlow, or ONNX. Build graph-level and kernel-level transformations for AI training and inference pipelines. Performance Tuning & System Analysis Conduct low-level profiling and performance tuning across compiler and runtime layers. Identify and eliminate bottlenecks across CPU/GPU/NPU workloads. Develop parallel programming solutions leveraging SIMD, multi-threading, and heterogeneous computing. Tooling & Infrastructure Develop tooling for performance analysis, debug, and test automation. Contribute to internal SDKs and devkits used by AI researchers and system engineers. Required Skills & Experience: Strong compiler development experience using LLVM, MLIR, Glow, or similar toolchains. Proficiency in C/C++, with solid command of Python for tooling and automation. In-depth understanding of compiler internals, including IR design, lowering, codegen, and scheduling. Deep knowledge of hardware-software co-design, particularly for AI/ML workloads. Experience with runtime systems, memory models, and performance modeling. Solid grasp of parallel and heterogeneous computing paradigms. Nice to Have: Experience working with custom AI hardware or edge inference platforms. Familiarity with quantization, scheduling for dataflow architectures, or compiler autotuning. Contributions to open-source compiler projects (e.g., LLVM, MLIR, TVM). Qualifications: Bachelors or Masters degree in Computer Science, Electrical Engineering, or a related field. 10–15 years of relevant hands-on experience in compilers, systems programming, or AI infrastructure. Contact: Uday Mulya Technologies muday_bhaskar@yahoo.com "Mining The Knowledge Community"

Senior Software Technical Director / Software Technical Director Bangalore Founded in 2023,by Industry veterans HQ in California,US We are revolutionizing sustainable AI compute through intuitive software with composable silicon We are looking for a Software Technical Director with a strong technical foundation in systems software, Linux platforms, or machine learning compiler stacks to lead and grow a high-impact engineering team in Bangalore. You will be responsible for shaping the architecture, contributing to codebases, and managing execution across projects that sit at the intersection of systems programming, AI runtimes, and performance-critical software. Key Responsibilities: Technical Leadership: Lead the design and development of Linux platform software, firmware, or ML compilers and runtimes. Drive architecture decisions across compiler, runtime, or low-level platform components. Write production-grade C++ code and perform detailed code reviews. Guide performance analysis and debugging across the full stackfrom firmware and drivers to user-level runtime libraries. Collaborate with architects, silicon teams, and ML researchers to build future-proof software stacks. Team & Project Management: Mentor and coach junior and senior engineers to grow technical depth and autonomy. Own end-to-end project planning, execution, and delivery, ensuring high-quality output across sprints/releases. Facilitate strong cross-functional communication with hardware, product, and other software teams globally. Recruit and grow a top-tier engineering team in Bangalore, contributing to the hiring strategy and team culture. Required Qualifications: Bachelors or Master’s degree in Computer Science, Electrical Engineering, or related field. 18+ years of experience in systems software development with significant time spent in C++, including architectural and hands-on roles. Proven experience in either: Linux kernel, bootloaders, firmware, or low-level platform software, or Machine Learning compilers (e.g., MLIR, TVM, Glow) or runtimes (e.g., ONNX Runtime, TensorRT, vLLM). Excellent communication skills—written and verbal. Prior experience in project leadership or engineering management with direct reports. Highly Desirable: Understanding of AI/ML compute workloads, particularly Large Language Models (LLMs). Familiarity with performance profiling, bottleneck analysis, and compiler-level optimizations. Exposure to AI accelerators, systolic arrays, or vector SIMD programming. Why Join Us? Work at the forefront of AI systems software, shaping the future of ML compilers and runtimes. Collaborate with globally distributed teams in a fast-paced, innovation-driven environment. Build and lead a technically elite team from the ground up in a growth-stage organization. Contact: Uday Mulya Technologies muday_bhaskar@yahoo.com "Mining The Knowledge Community"