model-quantization jobs in San Diego

Design, develop, and optimize machine learning systems for production AI platforms, focusing on model development, inference optimization, and scalable ML infrastructure. Responsibilities include building ML pipelines and frameworks, optimizing model inference for latency and cost, integrating LLMs into APIs and microservices, and designing data pipelines for preprocessing and feature engineering.…See more The role requires strong software engineering fundamentals combined with deep ML expertise, working across PyTorch/TensorFlow, model-serving systems, distributed computing, and GPU environments.

Design, develop, and optimize production machine learning systems including model development, inference optimization, and scalable ML infrastructure. Build end-to-end ML pipelines from training through deployment, optimize model inference for latency and cost, integrate LLM/ML models into APIs and microservices, and engineer data pipelines for preprocessing and validation.…See more Requires strong software engineering fundamentals with deep ML expertise, proficiency in Python and systems languages, and experience with ML frameworks, model serving, and distributed computing.

Lead AI software strategy and architecture for Qualcomm's automotive platform, directing deployment of computer vision, perception systems, LLMs, and generative AI workloads. Drive architectural decisions across multiple products and customer programs while mentoring cross-functional teams.…See more Ensure solutions meet automotive safety, performance, and reliability standards. Requires 8+ years of software/systems engineering experience with deep expertise in AI/ML, C/C++, and automotive software environments.

Design, architect, and deploy on-device AI prototype software for edge computing applications at Qualcomm's AI Research division. You will work with modern deep learning frameworks (PyTorch), optimize neural network models for embedded deployment, and integrate cutting-edge AI technologies into mobile, IoT, and autonomous systems.…See more The role requires strong Python and C/C++ skills, deep learning expertise, embedded software development experience, and solid software engineering fundamentals.

Senior ML engineer role focused on architecting, designing, and deploying on-device AI prototype software at the edge. Requires strong Python and C/C++ skills, deep learning expertise (PyTorch), and embedded software development experience.…See more The role involves model fine-tuning, quantization, hardware acceleration, and inference optimization for deployment on smartphones, autonomous vehicles, IoT, and robotics platforms. Must have 3+ years relevant experience and a strong theoretical ML background combined with modern software engineering practices.

Senior Machine Learning Engineer responsible for architecting, designing, developing, and deploying on-device AI prototype software for edge computing applications. The role requires expert-level proficiency in C++ and Python, deep knowledge of ML frameworks like PyTorch, and hands-on experience with low-level system debugging, performance tuning, and native inference driver development.…See more You will work in a multi-disciplinary research team advancing generative AI technology for the edge, including model fine-tuning, hardware acceleration, model quantization, and edge inference. A strong theoretical background in deep learning combined with embedded software development experience and modern software engineering best practices is essential.

Qualcomm is seeking an AI Performance Engineer to optimize and deploy machine learning models for inference acceleration on cloud AI hardware. The role involves converting and optimizing language models, vision models, and diffusion models using PyTorch and ONNX, analyzing performance bottlenecks, and designing efficient kernels in Triton.…See more You will collaborate with compiler, firmware, and platform teams to map next-generation AI workloads onto hardware, working across the full product lifecycle from research to commercial deployment. The position requires deep expertise in transformer architectures, inference optimization techniques, computer architecture, ML accelerators, and distributed systems.

Join Qualcomm AI Research as a Machine Learning Engineer to architect, design, develop, and deploy on-device AI prototype software for edge devices. You will work with a multi-disciplinary team using cutting-edge AI frameworks, focusing on model fine-tuning, quantization, and edge inference.…See more Strong Python, deep learning frameworks (PyTorch), object-oriented design, and software engineering best practices are required. Preferred qualifications include generative AI knowledge, neural network optimization, NPU/ML accelerator experience, Android programming, C/C++, and embedded software skills.

As a Staff Product Data Manager for AI Platforms & Systems Solutions at Qualcomm, you will own the product strategy, definition, and lifecycle for AI platform products and specifications spanning model enablement, agentic systems, orchestration runtimes, and open protocols. You will develop comprehensive plans of record including schedules, budgets, and resources, directing products from conception through end of life while functioning as a central resource across systems, engineering, standards, quality, marketing, and partner teams.…See more The role requires deep fluency in the Windows edge-AI stack, with technical depth across AI/ML models, agentic systems, orchestration engines, standards development, and hands-on experience delivering Windows-based AI products leveraging local accelerators (CPU, GPU, NPU). You will translate emerging model, agentic, and orchestration trends into differentiated product and specification roadmaps.

Design, develop, and optimize machine learning systems and models for production AI platforms, with focus on inference optimization, scalable ML infrastructure, and deployment. Responsibilities include building ML pipelines, optimizing model inference across hardware environments, integrating LLMs and models into APIs and microservices, designing data pipelines for ingestion and feature engineering, and collaborating cross-functionally on end-to-end ML solutions.…See more Requires strong software engineering fundamentals combined with deep ML expertise, proficiency in Python and at least one systems language (C++, Rust, or Go), and solid understanding of ML frameworks, transformer architectures, and model deployment systems.