About Dialpad
Dialpad is the AI-native business communications platform, unifying calling, messaging, meetings, and contact center on a single platform powered by AI that understands every conversation in real time. Over 70,000 companies globally rely on Dialpad for real-time, AI-driven insights. Dialpad is leading the shift to Agentic AI, with the DAART initiative redefining communications platforms.
Being a Dialer
At Dialpad, AI is central to how teams work, providing powerful tools to move faster, think bigger, and achieve more. Every conversation matters, and the platform turns these into insight and action. We seek intensely curious individuals who operate at a high level and embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic.
Your role
We are hiring AI Systems Engineers to build the machinery that connects AI training outputs to deployable artifacts, runtime systems to safe release, quality claims to evidence, and ambitious AI plans to functional systems. This is an implementation-heavy, building-focused engineering role on a small team responsible for making in-house AI capabilities easier to package, evaluate, deploy, promote, operate, and improve.
Strong candidates will help reduce the time and friction required to get in-house AI capabilities into reliable and scalable production while preserving operational discipline and truthful quality judgment. AI Platform Engineering's mission is to shorten the path from emerging AI capability to reliable production impact by building shared systems, standards, and delivery pathways. This new team is shaping the systems, interfaces, and standards for a growing AI organization to repeatedly deliver real capability into production.
What you’ll do
You will help design, build, and improve the systems that connect AI capability development to production reality. Depending on your strengths, this may include:
- Improving how model and capability artifacts are packaged, versioned, promoted, and rolled back.
- Building or improving deployment and release pathways for AI-backed services.
- Enabling shadow-serving, staged rollout, and candidate-versus-incumbent comparison.
- Strengthening runtime behavior, observability, and debugging for model-backed systems.
- Building or automating evaluation systems that make release decisions evidence-based.
- Reducing bespoke coordination and strengthening the shared rails used by multiple AI teams.
Your work should make the broader AI Platform organization faster, safer, and more effective at turning in-house AI capability into production reality.
Skills you’ll bring
- Bachelor's degree in Computer Science, Engineering, or equivalent related experience.
- 2 to 6 years of professional software engineering experience, with a proven track record of shipping production infrastructure or real systems that matter.
- Experience in writing solid, maintainable production code and applying strong software engineering fundamentals to solve complex debugging challenges.
- Experience in operating within ambiguous, cross-functional environments where requirements evolve and interfaces are real.
- Expertise in building for reproducibility, operability, and rollout safety, focusing on the quality of change rather than just local implementation.
Nice to have
- Experience with cloud infrastructure, containerized environments, managed ML platforms, or service orchestration systems.
- Experience with model serving, deployment systems, experiment tracking, artifact/version management, or ML lifecycle tooling.
- Experience with distributed systems, service platforms, search/relevance systems, internal enablement tooling, or production AI platforms.
- Experience with testing, benchmarking, experimentation systems, or evaluation frameworks that informed release decisions.
- Exposure to applied AI, speech, conversational systems, customer-facing workflows, or other production ML domains.