Software Engineer, ML Inference Platform Dialpad is the AI platform for customer experience, built to resolve customer problems in real time across voice and digital. Our AI agents learn from your best human agents and improve with every interaction, helping organizations understand their customers, deliver better experiences, increase operational efficiencies, and build a lasting competitive advantage. Unlike legacy systems built to route and answer, or standalone agentic bot vendors built to deflect, Dialpad was built to resolve.

Our AI agents and human agents operate on a single platform with shared context, allowing Agentic AI to resolve issues, advance deals, and eliminate busywork through automation while seamlessly handing conversations to humans when needed, with full context preserved.

Being a Dialer: At Dialpad, AI isn’t just a feature; We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves. We are hiring ML Inference Platform Engineers to help build that machinery.

This role is for engineers who like consequential junctions: between training outputs and deployable artifacts, between runtime systems and safe release, between quality claims and evidence, and between ambitious AI plans and systems that can actually carry them. It 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 may come from different technical backgrounds.

Some will lean toward runtime and serving. Some will lean toward evaluation and quality systems.

It is the ability to help move the same bottleneck: reducing 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 exists to shorten the path from emerging AI capability to reliable production impact. We build the shared systems, standards, and delivery pathways that let in‑house models and AI capability packages move from candidate state into observable, rollback‑safe production operation.

We enable the broader AI Platform division by making it faster and safer to ship new capabilities, improve existing ones, and learn from production behavior. The work is highly consequential, highly practical, and closely tied to the company’s broader AI strategy. We are not building one‑off demos or isolated launches.

We are building the machinery by which a growing AI organization can repeatedly deliver real capability into production. You will help design, build, and improve the systems that connect AI capability development to production reality. Building or improving deployment and release pathways for AI‑backed services.

Enabling shadow‑serving, staged rollout, and candidate‑versus‑incumbent comparison. Building or automating evaluation systems that make release decisions evidence‑based. Reducing bespoke coordination and strengthening the shared rails used by multiple AI teams.

What will not vary is the mission: your work should make the broader AI Platform organization faster, safer, and more effective at turning in‑house AI capability into production reality. 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 solid software engineering fundamentals to solve complex debugging challenges. ~ Expertise in building for reproducibility, operability, and rollout safety, focusing on the quality of change rather than just local implementation.

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. Within the range, individual pay is determined by work location and additional factors, including job‑related skills, experience, and relevant education or training.

Please note that the compensation details listed in Ontario role postings reflect the base salary only, and do not include bonus, equity, or benefits. $111,000 - $133,500 CAD Work at the center of the AI transformation in business communications Build and ship agentic AI products that are redefining how companies operate Join a team where AI amplifies every employee’s impact Competitive salary, comprehensive benefits, and real opportunities for growth Dialpad offers competitive benefits and perks, cutting‑edge AI tools, and a robust training program that help you reach your full potential. We are dedicated to creating a community of inclusion and an environment free from discrimination or harassment. In‑Office Requirement: This role is 100% on‑site and requires daily attendance at our office located at 137 Glasgow St, Kitchener.

📌 Software Engineer, ML Inference Platform (Waterloo)
🏢 Dialpad
📍 Waterloo