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# Engineer, AI Platforms

- **Company:** Qualcomm
- **Location:** San Diego, CA, United States
- **Experience:** Expert
- **Salary:** $160,000.0 - $210,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Continuous Integration, Distributed Systems, Python (Programming Language), Performance Tuning, Azure Machine Learning, Cloud Platform System, Data Ingestion, Containerization, Deployment Automation, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, Software Version Control, Data Pipelines, Microservices
- **Published:** September 17, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2969454010-2

## About the Role

* Python
 * C++Machine learning frameworks (Tensor
 * Flow, Py
 * Torch, ONNX)
 * MLOps and CI/CD for MLKubernetes and containerization
 * Distributed systems and microservices
 * Cloud platforms (AWS, Azure, or GCP)
 * GPU/accelerator optimization
 * Data pipelines and ETLMonitoring, observability, and performance tuning

## Description

Qualcomm seeks a Sr. Engineer, AI Platforms to design, build, and optimize large scale AI platforms that power next generation wireless, 5G, and connected devices. You will architect end to end AI/ML pipelines, integrate models into Qualcomm chipsets and cloud/edge environments, and ensure high performance, reliability, and security. Partner with silicon, software, and product teams to deliver scalable inference services, tools, and SDKs. Responsibilities include model deployment, performance tuning on heterogeneous hardware, MLOps automation, monitoring, and contributing to technical strategy in a fast paced, innovation driven culture.

Responsibilities

 * Architect and develop large-scale AI/ML platforms and services for wireless and 5
 * G applications
 * Design and implement end-to-end AI pipelines from data ingestion to deployment and monitoring
 * Optimize AI workloads for Qualcomm chipsets, heterogeneous compute, and edge/cloud environments
 * Collaborate with silicon, software, and product teams to integrate AI into commercial products
 * Implement MLOps practices including CI/CD, model versioning, and automated deployment
 * Monitor and improve platform performance, reliability, scalability, and security
 * Contribute to technical strategy, platform roadmap, and best practices for AI engineering
 * Create tools, SDKs, and APIs to enable internal teams and external partners
 * Troubleshoot complex production issues across distributed systems and accelerators
 * Document architectures, designs, and operational runbooks


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