Job Description
Parallel Wireless is reimagining mobile networks with innovative, energy-efficient Open RAN solutions. We are now embedding agentic AI deep into our engineering lifecycle — from feature development to field debugging — to accelerate delivery and raise quality across our 4G/5G stack.
Role Summary
We’re looking for a hands-on QA-Dev Engineer / Technical Lead to help redefine how Open RAN software is built and validated — by putting agentic AI at the center of our engineering workflow.
You’ll design and run the agent-driven systems that handle the time-intensive parts of RAN engineering: agents that author test plans and scripts against 3GPP specifications, execute 4G/5G test campaigns and continuously monitor lab setups, triage logs and perform root-cause analysis when failures arise, and generate PRVT builds to validate proposed fixes — all at a scale humans alone can’t sustain.
What you bring is deep RAN expertise — protocol stack, call flows, end-to-end systems — paired with strong judgment for what AI agents can and cannot be trusted to do. The result: faster fix cycles, broader test coverage, and engineers freed to focus on the hard, novel problems.
Parallel Wireless is reimagining mobile networks with innovative, energy-efficient Open RAN solutions. We are now embedding agentic AI deep into our engineering lifecycle — from feature development to field debugging — to accelerate delivery and raise quality across our 4G/5G stack.
Role Summary
We’re looking for a hands-on QA-Dev Engineer / Technical Lead to help redefine how Open RAN software is built and validated — by putting agentic AI at the center of our engineering workflow.
You’ll design and run the agent-driven systems that handle the time-intensive parts of RAN engineering: agents that author test plans and scripts against 3GPP specifications, execute 4G/5G test campaigns and continuously monitor lab setups, triage logs and perform root-cause analysis when failures arise, and generate PRVT builds to validate proposed fixes — all at a scale humans alone can’t sustain.
What you bring is deep RAN expertise — protocol stack, call flows, end-to-end systems — paired with strong judgment for what AI agents can and cannot be trusted to do. The result: faster fix cycles, broader test coverage, and engineers freed to focus on the hard, novel problems.