Founding AI Systems Engineer

Dialpad•Kitchener, ON

6d•$132,000 - $156,750

About The Position

Dialpad is seeking founding AI Systems Engineers to help build the core machinery for their AI platform. 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. The AI Platform Engineering team's mission is to shorten the path from emerging AI capability to reliable production impact by building shared systems, standards, and delivery pathways. This founding team will shape the systems, interfaces, and standards, working on highly consequential and practical tasks closely tied to the company's AI strategy. The goal is to build the infrastructure for a growing AI organization to repeatedly deliver real capability into production.

Requirements

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 Haves

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.

Responsibilities

Design, build, and improve systems that connect AI capability development to production reality.
Improve how model and capability artifacts are packaged, versioned, promoted, and rolled back.
Build or improve deployment and release pathways for AI-backed services.
Enable shadow-serving, staged rollout, and candidate-versus-incumbent comparison.
Strengthen runtime behavior, observability, and debugging for model-backed systems.
Build or automate evaluation systems that make release decisions evidence-based.
Reduce bespoke coordination and strengthen the shared rails used by multiple AI teams.