Senior AI Engineer (AI/ML Inference)

Dialpad; Vancouver; On-site; CAD 140,000 - 200,000; Full time

Benefits offered by this job

Stock options

100% medical, dental, vision

Continuing education stipend

Cell phone & home internet stipend

Catered lunches & snacks

Work from home opportunities

Job summary

Dialpad seeks a Sr. AI Engineer: Systems to own production systems turning speech models and third-party capabilities into reliable, scalable experiences for AI voice agents.

You will work at the intersection of speech, ML infrastructure, and product engineering, building APIs, services, deployment workflows, and robust operational foundations to meet uptime and latency SLAs.

Qualifications

• Strong software engineering fundamentals with Python expertise and API/service design experience.
• 5+ years in production software, ML-backed systems, real-time or latency-sensitive domains.
• Ability to ship safe, observable, cost-efficient production systems for speech/voice agents.

Responsibilities

• Productionize speech models and third-party capabilities for real-time use.
• Own APIs, services, deployment workflows and integration layers.
• Ensure uptime, latency SLAs, and reliability with observability and incident response.
• Collaborate with MLOps, speech, platform, and product teams; mentor engineers.

Skills

Python proficiency

Backend engineering

ML infrastructure

Observability

SLOs & latency

Cloud & distributed systems

Model serving

Real-time systems

Tools

GCP

CI/CD

Containers

Orchestration

Job description

• As a Sr. AI Engineer: Systems, you’ll serve as an embedded senior back-end engineer on our Speech Team, owning the production systems that turn speech models and third-party capabilities into reliable, scalable experiences for Dialpad’s AI voice agents. You’ll work at the intersection of speech, ML infrastructure, and product engineering: productionizing models, enabling self-hosted inference, integrating external APIs, and building the operational foundations required for strong uptime and latency SLAs
• You’ll partner closely with the MLOps (Inference) team while bringing deep ownership of the speech domain, helping the team move quickly from promising model or vendor capability to safe, observable, and cost-effective production. This role offers broad technical influence and the opportunity to shape how Dialpad operates real-time speech systems at scale
• This position reports to our Senior Manager, AI Speech, and offers the opportunity to be based in our Canada Hub locations
• Productionization & Service Ownership: Own the path from speech model or third-party capability to production, building the APIs, services, deployment workflows, and integration layers that make it safe and easy for the Speech Team to ship improvements
• Self-Hosted Inference & Scaling: Productionize and operate self-hosted speech models, optimizing serving architecture, resource utilization, concurrency, autoscaling, and cost so they can meet the demands of real-time voice agents
• Third-Party APIs & Provider Resilience: Integrate and maintain third-party speech APIs behind durable abstractions, with clear failover, capacity planning, version management, and vendor-performance monitoring
• Reliability, SLOs & Observability: Build the monitoring, alerting, dashboards, health checks, and incident-response practices needed to meet uptime, latency, and quality SLAs for customer-facing speech systems
• Release & Evaluation Infrastructure: Partner with Speech and MLOps engineers to enable shadow traffic, staged rollouts, model and artifact versioning, rollback-safe releases, and candidate-versus-incumbent comparisons
• Cross-Functional Leadership & Mentorship: Work closely with the MLOps (Inference) team and partner teams across speech, platform, telephony, and product to set technical direction, mentor engineers, and turn model advances into reliable production impact

Benefits

• Company stock options
• 100% paid medical, dental, and vision plan
• Continued education stipend
• Cell phone, home internet, and gym membership stipend
• Catered lunches, free snacks & drinks
• Work from home opportunities

Reliability & Operations: Strong understanding of observability, alerting, incident response, capacity planning, and availability and latency SLAs for customer-facing systemsCloud & Distributed Systems: Experience with cloud infrastructure and distributed systems, as well as familiarity with containers, orchestration, service networking, CI/CD, and GCPSystems & Backend Engineering: Strong software engineering fundamentals and proficiency in Python, plus experience designing maintainable APIs, services, and integration layers. We’re open to candidates who are strongest in backend/platform engineering or who have grown from ML into systemsCross-Functional Technical Leadership: Demonstrated ability to work closely with ML scientists, MLOps and inference engineers, and product teams, mentor teammates, and make pragmatic trade-offs across quality, reliability, latency, scale, and costModel Serving & Inference: Hands-on experience deploying, scaling, and troubleshooting ML models in production, including model serving, inference optimization, resource management, and safe model and version rolloutsProduction ML & Streaming: 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