Software Engineer, ML Inference Platform

Software Engineer, ML Inference Platform

AI Engineering Kitchener, Canada

About Dialpad

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.

Market-leading brands, including Randstad, Motorola Solutions, Netflix, the San Diego Padres, the Colorado Rockies Baseball Club, and Cal Athletics, trust Dialpad. Dialpad is backed by Andreessen Horowitz, GV, ICONIQ Capital, and T‑Mobile.

Being a Dialer

At Dialpad, AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more.

We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves.

We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic.

Your role

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.

This is not a research role, and it is not a generic support role. 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 be strongest in productionization and platform systems. Some will lean toward runtime and serving. Some will lean toward evaluation and quality systems. What unifies them is not one toolchain or one narrow specialty. 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. Our work sits at the junction between model development, runtime systems, evaluation, and delivery. 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.

This is a new team. The systems, interfaces, and standards are still being shaped. 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.

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, that may include work such as:

• 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.

The exact balance will depend on your background and the team’s evolving needs. 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.

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.

For exceptional talent based in Ontario, Canada the target base salary range for this position is posted below. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the target range for new hire salaries for the position. Within the range, individual pay is determined by work location and additional factors, including job‑related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in Ontario role postings reflect the base salary only, and do not include bonus, equity, or benefits.

Ontario Salary Range

$111,000-$133,500 CAD

Why Join Dialpad

• 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

We believe in investing in our people. Dialpad offers competitive benefits and perks, cutting‑edge AI tools, and a robust training program that help you reach your full potential. We have designed our offices to be inclusive, offering a vibrant environment to cultivate collaboration and connection. Our exceptional culture, repeatedly recognized as a Great Place to Work, ensures that every employee feels valued and empowered to contribute to our collective success.

Don’t meet every single requirement? If you’re excited about this role and possess the fundamental traits, drive, and strong ambition we seek, but your experience doesn’t meet every qualification, we encourage you to apply.

Dialpad is an equal‑opportunity employer. We are dedicated to creating a community of inclusion and an environment free from discrimination or harassment.

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