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Parallel Wireless seeks a Senior PHY Software Engineer with 10+ years of experience in cellular PHY software development, integration, debugging, and system troubleshooting. The role focuses on PHY development, debugging, and integration within our cellular products, with an emphasis on AI-assisted RCA and log analysis.
Based in Bengaluru, you will collaborate with MAC/RLC/RRC, RF, DSP, HW, and system teams to resolve complex product and field issues, driving improvements in performance and
Senior PHY Software Engineer – 2G/4G/5G
We are looking for a highly experienced Senior PHY Software Engineer with 10+ years of experience in cellular PHY software development, integration, debugging, and system troubleshooting across 2G, 4G, and 5G technologies.
The role will primarily focus on PHY development, debugging, issue resolution, and integration within our cellular products. As part of our ongoing effort to improve engineering productivity and RCA capabilities, the engineer will also contribute to our AI-assisted development and Root Cause Analysis initiative, helping build and fine-tune AI-based tools/agents for PHY log and crash analysis. The ideal candidate should be a strong PHY domain expert and problem solver, capable of going beyond linear debugging approaches and identifying root causes by correlating evidence across different sources and layers.
Exposure to AI/LLM-based developer tools or coding assistants.
Interest in applying AI to software development, debugging, log analysis, and RCA.
Experience with automation, log-processing, or internal debugging/analysis tools.
Willingness to learn and adopt AI-assisted development and agent-based troubleshooting approaches.
AI experience is not mandatory. Strong PHY domain expertise, development experience, and troubleshooting ability are the primary requirements.
Nice to Have:
The initial focus will be on understanding the existing PHY implementation, logs, failure patterns, and troubleshooting methodologies. Over time, the engineer will contribute to building and improving AI-assisted RCA capabilities and use these capabilities as part of regular PHY development, debugging, and bug-fixing activities.
The objective is to combine deep PHY expertise with AI-assisted engineering to improve debugging efficiency, RCA accuracy, and overall product quality.