Staff Engineer- AI/Machine Learning R&D

Job description

**Job Area:** Engineering Group, Engineering Group > Machine Learning Engineering **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 Machine Learning Engineer, you will create and implement machine learning techniques, frameworks, and tools that enable the efficient discovery and utilization of state-of-the-art machine learning solutions over a broad set of technology verticals or designs. Qualcomm Engineers collaborate with cross-functional teams to enhance the world of mobile, edge, auto, and IOT products through machine learning hardware and software. **Minimum Qualifications:** Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience; OR Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience; OR PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. **StaffApplication/ CustomerEngineering** **Role Overview** We are looking for a Staff Engineer with **strong domain expertise** in VLSI/EDA tools and workflows to drive adoption, usability, and reliability of internal GenAI/Agentic AI tools and platform. This role requires deep hands-on usage, triaging expertise, and customer engineering experience, acting as the bridge between Physical Design Engineers/CAD teams, Software and Systems engineering team. **The ideal candidate has exposure to AI/GenAI systems and can understand and debug workflows involving EDA tools, AI agents, orchestration pipelines, and non-deterministic system behaviors.** **Key Responsibilities** - Own end-to-end user support and triage workflows; debug complex EDA environment, configuration and software integration issues - Diagnose workflow failures across toolchains, configurations, and infrastructure - Drive deep product understanding through hands-on tool usage across real workflows - Translate user requirements and issues into clear, actionable engineering inputs - Configure and onboard new projects and users; enable scalable adoption - Define and maintain real-world test cases and reproducible debugging scenarios - Collaborate with dev/QA teams to improve observability, debuggability, and stability - Debug AI/GenAI agent workflows, including orchestration failures, tool invocation issues, and non-deterministic behavior - Ensure tools operate in an agentic model with autonomous execution (MCP-based access) and minimal manual intervention - Define and validate clear functional specifications covering capabilities, inputs, outputs, and expected behavior - Drive structured enablement plans with phased onboarding, deliverables, acceptance criteria, and timelines - Develop comprehensive test strategies including shared and hidden testcases for robust validation - Measure tool performance using objective QoR metrics (TNS, WNS, congestion, runtime, memory) - Integrate tools into automated validation/regression pipelines for continuous testing and reporting - Enable usage telemetry collection (feature usage, runtime distribution, error trends) to support data-driven improvements **Required Skills Experience** - 9-12 years in Applications Engineering / Customer Engineering / System Testing roles with EDA tools / applications. - Strong domain knowledge in VLSI / Physical Design / EDA workflows - Knowledge of Agentic AI /MCP/RAG workflows - Proven expertise in triaging and debugging complex software/tool issues - Strong Python/Linux/scripting experience - Ability to independently run and debug end-to-end workflows - Strong problem decomposition and root-cause analysis skills - Excellent communication and cross-functional collaboration **Preferred Qualifications** - Experience with AI/GenAI tools, agent frameworks, or orchestration systems - Familiarity with evaluation, benchmarking, or validation pipelines - Exposure to CI/CD, large-scale systems and HPC environments - Experience improving workflows, UX, or developer productivity **Positioning** This is a hands-on Applications / Customer Engineering role with strong domain expertise, triage ownership, and AI/GenAI workflow understanding not a Product Manager role.