Silicon & Data Automation Systems Software Engineer

About the Role

Qualcomm is seeking a highly motivated and technically strong Staff Engineer, Silicon & Data Automation Systems to join a growing automation team supporting silicon and system yield engineering. This role is instrumental in expanding automation capacity within the silicon engineering organization and will be based in the Bangalore Design Center (BDC). The engineer will work closely with U.S.-based yield automation leadership and stakeholders. This is a senior individual contributor role focused on developing production-grade automation, data pipelines, database-backed applications, and engineering tools. The position emphasizes an AI-first engineering mindset, utilizing LLM-based coding agents and AI-assisted automation tools as productivity multipliers.

Key Responsibilities

• Automation Development & Production Engineering
• Design, develop, test, and maintain automation tools to improve silicon and system yield engineering workflows, including data processing, diagnostics, reporting, and operational efficiency.
• Build reliable Linux-based automation services, scripts, data loaders, and backend systems.
• Develop production-grade solutions with appropriate logging, monitoring, error handling, alerting, documentation, and operational supportability.
• Contribute to systems that are maintainable, fault-tolerant, secure, and suitable for long-running production use.
• Debug and resolve automation issues across Linux servers, databases, file systems, scheduled jobs, data feeds, and dependent engineering systems.
• Implement new automation capabilities under architectural guidance and develop deeper ownership of specific tools, workflows, or data domains.
• Data Systems & Database Automation
• Build and maintain data pipelines to collect, transform, validate, and load engineering and manufacturing data.
• Work with relational databases, SQL, schema design, query optimization, and database-backed automation workflows.
• Integrate data from multiple sources, including engineering systems, manufacturing data feeds, logs, reports, and analysis outputs.
• Develop tools to make complex data more accessible, verifiable, monitorable, and actionable.
• Ensure data quality, traceability, and operational robustness in automation.
• AI-Assisted Engineering & Automation Acceleration
• Utilize LLM-based coding agents and AI-assisted automation tools responsibly in daily engineering work.
• Apply AI-assisted development practices to improve code quality, accelerate troubleshooting, generate tests, improve documentation, and increase engineering output.
• Build or integrate AI-enabled engineering tools such as automation agents, internal assistants, MCP servers, and workflow accelerators.
• Maintain strong human review and engineering judgment for AI-generated code, SQL, analysis, documentation, and operational recommendations.
• Ensure AI-assisted workflows adhere to standards for security, correctness, maintainability, and production safety.
• Help establish practical patterns for using AI-first development methods in production engineering environments.
• Tooling, Applications & Workflow Integration
• Build backend tools, dashboards, web applications, APIs, command-line utilities, and scheduled automation.
• Utilize technologies including Python, shell scripting, SQL, Django or similar frameworks, REST APIs, log analytics tools, and enterprise data systems.
• Integrate automation into existing engineering workflows without disrupting production operations.
• Collaborate with engineers and stakeholders to identify manual pain points and automate them.
• Create clear documentation, usage guidance, and operational notes.
• Cross-Functional Collaboration
• Work with engineers across design, DFT, validation, diagnostics, and data domains to understand requirements and deliver solutions.
• Communicate technical findings, implementation options, risks, and tradeoffs clearly to both software and semiconductor engineering audiences.
• Participate in code reviews, design discussions, troubleshooting sessions, and production readiness reviews.
• Collaborate effectively across time zones, including regular overlap with U.S. Central Time morning hours.
• Contribute to a team culture focused on engineering rigor, accountability, automation leverage, and continuous improvement.

Requirements & Tech Stack

• Linux: Comfortable diagnosing runtime issues, managing scripts, and troubleshooting production systems.
• Data: Ability to handle complex engineering data and build reliable transformations with traceability.
• Databases: Strong SQL skills with practical understanding of schemas, performance, and data integrity.
• Automation: Ability to convert manual workflows into robust and scalable systems.
• Production Mindset: Builds systems that are observable, maintainable, secure, and resilient.
• AI-First Engineering: Uses AI tools to increase output while maintaining full accountability.
• Engineering Communication: Works effectively with domain experts and delivers practical solutions.
• Technologies:
• Python
• Shell Scripting
• SQL
• Django or similar frameworks
• REST APIs
• Log analytics tools
• Enterprise data systems
• Git or similar version control systems
• LLM-based coding agents / AI-assisted development tools

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Data Engineering, Software Engineering, or related field, or equivalent practical experience.
• Strong hands-on experience with Linux-based development and automation, including shell scripting, server-side troubleshooting, cron or scheduled jobs, file systems, permissions, and operational debugging.
• Strong programming skills in Python or a comparable language used for automation, data processing, backend systems, or engineering tools.
• Strong experience with SQL, relational databases, data modeling, query development, and database-backed applications.
• Experience building automation that processes, validates, transforms, or integrates large technical datasets.
• Experience developing production-quality software or automation with testing, logging, error handling, documentation, and maintainability.
• Experience using Git or similar version control systems in a collaborative environment.
• Practical experience using LLM-based coding agents or AI-assisted development tools to improve productivity.
• Ability to review AI-generated work critically and maintain ownership of correctness, security, reliability, and production readiness.
• Strong debugging and problem-solving skills across software, data, database, and Linux environments.
• Excellent written and verbal communication skills in English.
• Ability to work effectively with U.S.-based stakeholders with required time overlap.

Preferred Qualifications

• Experience with semiconductor design, DFT, validation, diagnostics, yield, or product engineering environments.
• Experience working with engineering datasets such as test data, DFT outputs, manufacturing data, diagnostic data, or high-volume silicon or system telemetry.
• Familiarity with semiconductor data formats such as STDF, KLARF, wafer maps, lot history, test results, binning, dispositions, or assembly data.
• Experience building automation for engineering teams in semiconductor, manufacturing, hardware, or similar domains.
• Experience with Oracle, PL/SQL, ODBC, database loaders, schema design, or production database integration.
• Experience with Django or similar backend frameworks.
• Experience with REST APIs, internal tools, dashboards, reporting systems, or workflow applications.
• Experience with log analytics, monitoring, Splunk, or observability tools.
• Experience with batch compute, distributed jobs, LSF or grid environments, or multi-server automation.
• Experience building or integrating AI-assisted tools, internal agents, MCP servers, or RAG workflows.
• Familiarity with responsible AI-assisted development practices, including human-in-the-loop review and secure handling of generated outputs.
• Experience working across global teams and time zones.