# Senior AI Engineer

**Company:** [Dialpad](https://hotfix.jobs/companies/dialpad)
**Location:** Vancouver, Canada
**Role:** ML Engineering
**Salary:** CA$185k – CA$214k/yr
**Experience:** 5+ years
**Skills:** Python, Machine Learning, Model Serving, Inference Optimization, APIs, Distributed Systems, GCP, Containers, Kubernetes, Service Networking, CI/CD, Observability, Autoscaling, Real-Time Systems, Speech Technology
**Posted:** 2026-09-04

> Senior AI engineer owning production systems for real-time speech models and AI voice agents. The role requires 5+ years of production software experience, Python, model serving and inference optimization, cloud and distributed systems expertise, and strong reliability and operations skills.

## Job Description

## Responsibilities
- Own productionization of speech models and third-party capabilities, including APIs, services, deployment workflows, and integration layers.
- Productionize and operate self-hosted speech models, optimizing serving architecture, resource utilization, concurrency, autoscaling, and cost.
- Integrate third-party speech APIs with durable abstractions, failover, capacity planning, version management, and vendor-performance monitoring.
- Build monitoring, alerting, dashboards, health checks, and incident-response practices for uptime, latency, and quality SLAs.
- Enable shadow traffic, staged rollouts, model and artifact versioning, rollback-safe releases, and candidate-versus-incumbent comparisons.
- Set technical direction, mentor engineers, and collaborate across speech, platform, telephony, product, and MLOps teams.

## Requirements
- 5+ years of experience building or operating production software, including ML-backed systems, real-time services, speech applications, streaming media, or other latency-sensitive systems.
- Strong software engineering fundamentals and proficiency in Python.
- Experience designing maintainable APIs, services, and integration layers.
- Hands-on experience deploying, scaling, and troubleshooting production ML models, including model serving, inference optimization, resource management, and safe model/version rollouts.
- Experience with cloud infrastructure and distributed systems.
- Familiarity with containers, orchestration, service networking, CI/CD, and Google Cloud.
- Strong understanding of observability, alerting, incident response, capacity planning, and availability and latency SLAs.
- Ability to collaborate with ML scientists, MLOps and inference engineers, and product teams, mentor teammates, and make trade-offs across quality, reliability, latency, scale, and cost.

## Compensation
- Base salary range: **$184,500–$213,750 CAD** for exceptional talent based in British Columbia, Canada.
- Compensation excludes bonus, equity, and benefits.

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