Job Overview

Qualcomm is hiring an AI and Machine Learning Engineer with strong embedded software expertise for its Hyderabad engineering team. This role focuses on AI inference software for embedded devices rather than model training, data science, or applied AI research. The engineer will work on bringing Generative AI capabilities to Qualcomm chipsets and improving the performance of neural network inference directly on edge devices.

The position combines advanced C/C++ software engineering, operating system concepts, AI inference optimization, hardware acceleration, and embedded development. You will contribute to the Qualcomm AI Runtime, helping developers execute modern neural network and Generative AI models efficiently on Snapdragon platforms. The work emphasizes low-latency performance, power efficiency, scalability, and reliable software integration across heterogeneous compute resources.

Key Responsibilities

• Develop and enhance large-scale C/C++ software components supporting AI inference on embedded platforms.
• Contribute to the development and commercialization of Qualcomm AI Runtime capabilities.
• Optimize algorithms and software workloads for CPU, GPU, and NPU-based AI acceleration.
• Analyze the performance characteristics of large AI models and improve inference efficiency on edge hardware.
• Apply software design patterns, object-oriented development principles, and operating system knowledge to production software.
• Work with Generative AI architectures, including LLMs, LVMs, and LMMs, and understand important inference building blocks such as self-attention, cross-attention, and KV caching.
• Apply floating-point, fixed-point, and quantization concepts when optimizing AI workloads.
• Investigate complex software and performance issues through detailed debugging and analytical techniques.
• Collaborate with systems, hardware, architecture, test, and other engineering teams to develop system-level solutions.
• Contribute to software intended for on-device AI execution where cloud connectivity is not required.
• Support engineering activities that help move AI inference technologies from development toward commercial products.

Required Skills

Strong embedded software experience is essential for this position. Candidates should have advanced C or C++ programming skills and a solid understanding of software design patterns and operating system concepts. Experience developing substantial software stacks and working close to embedded hardware will be important.

The role requires knowledge of AI inference and Generative AI concepts rather than traditional machine learning model training. Candidates should understand modern model structures such as large language models and the underlying concepts used during inference. Familiarity with attention mechanisms and KV caching will be valuable.

Experience optimizing algorithms for hardware accelerators such as CPUs, GPUs, and NPUs is required. A strong understanding of floating-point and fixed-point representations, quantization, and performance optimization is also expected.

Strong analytical and debugging ability is important because the work involves performance-sensitive embedded software and complex AI inference systems. Candidates should also be comfortable communicating technical ideas through written, verbal, and presentation formats.

Preferred Skills

Experience with SIMD processor architecture, system design, and kernel development for SIMD architectures is preferred. Familiarity with Linux and Windows environments is also useful.

Knowledge of AI and machine learning frameworks and inference runtimes can strengthen an application. Relevant technologies mentioned in the role include llama.cpp, MLX, MLC, PyTorch, TensorFlow Lite, and ONNX Runtime.

Experience with parallel computing and technologies such as OpenCL and CUDA is an advantage. Object-oriented software development experience is also relevant to the role.

Education

Candidates should have a Bachelor's, Master's, or PhD degree in Engineering, Information Systems, Computer Science, or a related technical discipline. The qualification requirements allow different experience levels depending on the degree earned. The role specifically describes multiple openings and separately indicates a target relevant experience range of 9 to 15 years.

Experience

The job posting indicates openings at multiple levels and describes 9 to 15 years of relevant software development experience for the stated role. Its minimum qualification section also recognizes candidates with a Bachelor's degree and 4+ years, Master's degree and 3+ years, or PhD and 2+ years of software engineering experience. Strong embedded software experience is mandatory, and the role is specifically intended for AI inference SDK development on embedded systems.

Required Technologies

Core technologies and technical areas include C, C++, embedded systems, AI inference, Generative AI, LLMs, LVMs, LMMs, Qualcomm AI Runtime, CPU, GPU, NPU, SIMD architecture, operating systems, design patterns, object-oriented programming, floating-point computing, fixed-point computing, quantization, and AI hardware acceleration.

The preferred technology stack includes Linux, Windows, llama.cpp, MLX, MLC, PyTorch, TensorFlow Lite, ONNX Runtime, OpenCL, and CUDA. Knowledge of kernel development and parallel computing is also relevant.

Soft Skills

The role requires clear communication, strong analytical thinking, and advanced debugging ability. Engineers will work with globally distributed teams and multiple technical stakeholders, so collaboration and the ability to explain complex engineering decisions are important.

Candidates should be comfortable working across software, hardware, architecture, systems, and testing functions. A practical problem-solving approach and attention to performance-sensitive implementation details will be valuable.

Benefits of Working in this Role

This position provides an opportunity to work on edge AI and embedded inference technologies that bring Generative AI capabilities directly to devices. Engineers can deepen their expertise in C/C++, AI acceleration, embedded systems, model inference, quantization, operating systems, and performance optimization.

The role also offers exposure to Qualcomm AI Runtime and Snapdragon-based computing, providing experience at the intersection of advanced AI software and specialized hardware.

Work Mode

The provided job description does not specify a remote or hybrid work arrangement. The role is a full-time position based in Hyderabad.

Location

Hyderabad, Telangana, India.

Who Should Apply

This opportunity is suitable for experienced embedded software engineers, C++ engineers, AI inference engineers, machine learning systems engineers, and engineers specializing in performance optimization for AI hardware.

Candidates should have strong embedded development experience and be comfortable working with C/C++, operating systems, hardware accelerators, and AI inference workloads. Professionals with experience in SIMD, GPU/NPU optimization, quantization, kernel development, or embedded AI runtimes should consider this role.

This is not positioned as a data science, applied AI, or machine learning model training role. Candidates should focus their application on embedded AI inference, software engineering, runtime development, and hardware-aware optimization.

Career Growth

Working on embedded AI inference can help experienced engineers build deeper expertise in edge AI, AI runtimes, heterogeneous computing, embedded systems, performance engineering, and Generative AI deployment. Experience across C++, AI accelerators, operating systems, and model inference can also support progression into senior engineering, technical leadership, architecture, and specialized AI systems roles.

Application Advice

When applying for this Qualcomm AI and Machine Learning Engineer opportunity, emphasize hands-on embedded software development and substantial C/C++ experience. Highlight projects involving AI inference, hardware acceleration, CPU/GPU/NPU optimization, SIMD architectures, operating systems, kernel development, or performance tuning.

If you have worked with LLMs, LVMs, LMMs, attention mechanisms, KV caching, quantization, floating-point or fixed-point computation, include those details clearly. Experience with llama.cpp, MLX, MLC, PyTorch, TensorFlow Lite, ONNX Runtime, OpenCL, or CUDA should also be visible in the technical skills section.

Candidates targeting Qualcomm Jobs, AI Jobs, Machine Learning Jobs, Embedded Jobs, C++ Jobs, Software Engineering Jobs, Hyderabad Jobs, Edge AI Jobs, AI Inference Jobs, and Developer Jobs should demonstrate how their experience aligns with production embedded AI systems rather than model-training responsibilities.