Junior Software Engineer, AI Agent Platform

Company Overview At Motorola Solutions, we believe that everything starts with our people. We’re a global close-knit community, united by the relentless pursuit to help keep people safer everywhere. We build and connect technologies to help protect people, property and places. Our solutions foster the collaboration that’s critical for safer communities, safer schools, safer hospitals, safer businesses, and ultimately, safer nations. Connect with a career that matters, and help us build a safer future.

Aperçu de l’entreprise Chez Motorola Solutions, nous pensons que tout commence par nos employés. Nous sommes une communauté mondiale soudée, unie par la volonté incessante de contribuer à la sécurité des personnes partout dans le monde. Nous concevons et connectons des technologies qui contribuent à protéger les personnes, les biens et les infrastructures. Nos solutions favorisent la collaboration essentielle pour améliorer la sécurité des populations, des écoles, des hôpitaux, des entreprises et, finalement, des nations. Connectez-vous à une carrière qui compte et aidez-nous à bâtir un avenir plus sûr.

Department Overview Hyper, a Motorola Solutions Company, is a leader in conversational, agentic AI designed to reduce the burden on understaffed public safety answering points (PSAPs) by handling non-emergency calls. It is revolutionizing emergency response systems through cutting-edge AI technologies. Our first product is reducing 911 wait times by alleviating administrative work from highly trained 911 telecommunicators.

Job Description We’re looking for a Software Engineer to join the AI Agent Platform team behind Hyper’s emergency response products (Motorola Solutions announced that it acquired Hyper on April 9, 2026).

You’ll work alongside experienced engineers across real-time AI pipelines, telephony, backend infrastructure, and product surfaces. You’ll help influence architecture, build core product features, and shape how our team uses AI agents to move faster without compromising reliability, security, or product quality. We encourage engineers to use AI agents in their own work. But because this is mission-critical software, humans still own quality, judgment, and what ships.

What You’ll Do

• Build real-time voice AI features for emergency response workflows.
• Design and operate LLM-powered systems that support human 911 telecommunicators.
• Own features across the stack, from backend services and AI pipelines to frontend interfaces and internal tools.
• Work on low-latency audio processing, streaming systems, telephony integrations, and human-in-the-loop AI workflows.
• Help integrate speech models, LLMs, and emerging AI technologies into production systems.
• Build strong habits around tests, code review, observability, and security from day one.
• Help write and improve evaluation, testing, and observability for AI-assisted systems.
• Participate in on-call responsibilities (with support and shadow rotations to start), backed by clear runbooks and blameless post-incident reviews.
• Ask questions, dig into how things work, and bring fresh perspectives to the team.

You Might Be a Fit If

• You’re curious about working across backend, infrastructure, AI, and product, even if you haven’t done it all yet.
• You use AI tools daily, but you do not confuse speed with quality.
• You care about quality — you write tests, read documentation, and try to understand a system before you change it.
• You take feedback well and act on it.
• You have strong product instincts and care about the humans using what you build.
• You thrive in a small, fast-moving team where you’ll have real ownership early — and you’re comfortable asking for help.
• You want to work on AI systems that matter beyond demos.

Relevant Experience

We don’t expect you to have shipped production systems. We care most about engineering fundamentals, learning velocity, curiosity, and how you think. Coursework, internships, research, and personal projects all count.

Relevant Exposure May Include

• Projects involving LLMs, AI agents, AI pipelines, RAG systems, evals, or prompt test harnesses.
• Speech-to-Text, Text-to-Speech, Voice Activity Detection, endpointing, interruption handling, noise filtering, diarization, or audio streaming.
• Telephony and voice infrastructure, including PSTN, SIP trunking, PBX, and call routing.
• Real-time communication systems using WebRTC, WebSoc…