Staff Engineer- Eda Ml Tools - Customer Engineering(Physical Design)

Company: Qualcomm India Private Limited

Job 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.

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 softwareintegrationissues
• 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 and Experience

• 9-12 years in Applications Engineering / Customer Engineering (preferably in Physical Design (PNR/Floor planning))
• 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.