Software Engineer II, AI v Česko

In the time it takes you to read this job description, RapidSOS will have handled ~1,380 emergencies. At RapidSOS, we are committed to using technology to build a safer, stronger future and working together to save lives. We’re in an exciting phase of growth, welcoming new members from across the globe to our mission-driven, ambitious, and inclusive team. Our work is founded on our values of trust and safety , pioneering , urgency , and purpose over pride , all of which support a company culture where people can innovate, collaborate, grow, and, above all, make an impact.   RapidSOS is the leading public safety AI company that unlocks mission-critical intelligence for emergency response. It harnesses artificial and human intelligence to fuse life-saving data from the world’s largest safety network, which includes over 700M+ connected devices and 200+ global technology companies. This intelligence is delivered to 23,000+ federal, state, and local agencies, serving one million first responders. RapidSOS has supported over one billion emergencies in 16 countries, powering RapidSOS HARMONY, the first purpose-built AI for public safety, designed to save critical time by automatically detecting emergencies, unifying real-time data and video streams, and coordinating a faster, more effective response. Learn more at RapidSOS. com .

What this role is about: Our Voice AI team is building the next generation of intelligent safety technology. We build AI agents that triage incoming alerts in seconds, relieve the burden on emergency telecommunicators by handling non-emergency calls, and provide real-time translation between callers and call-takers. As an AI Engineer on this team, you’ll own core capabilities end to end—from model behavior and backend architecture to evaluation tooling. Every improvement you ship shaves seconds off a response, and in an emergency, seconds are what save lives. What you’ll do:  - End-to-end product delivery: Own Voice AI agent(s) capabilities from problem discovery and rapid experimentation through architecture, implementation, testing, deployment, and measurement of impact across the real-time agent, APIs, web application, and cloud infrastructure. - Improve conversational quality: Improve the caller experience—including latency, turn-taking, barge-in, speech recognition, multilingual behavior, protocol adherence, and transfers—using production evidence and reproducible experiments. - Safety and reliability: Design fail-safe behaviors, strengthen service resilience and observability, and respond to production issues through contributing factors  analysis, mitigation, and prevention. - Evaluation and continuous improvement: Build and use automated evaluations, call-replay tools, production metrics, and controlled traffic trials to identify regressions, quantify trade-offs, and improve model and system behavior. - Work across the full stack, from Python backend and AI model behavior to the React/TypeScript UI that customers and operators use to configure and monitor their AI agents. - Use AI tools daily to move faster on code, testing, call analysis, and incident triage, while keeping full ownership of architecture, safety boundaries, and production decisions.