Senior Machine Learning Developer

About the Role

Build and operate conversational AI systems that handle customer interactions over phone and SMS. This role spans product and ML platform work, from developing LLM-powered features to creating the tooling and evaluation systems that help teams deliver reliable production experiences.

Compensation & Benefits

Base salary range: CAD 200,000 to CAD 250,000 annually.

Location

On-site in Montreal, Quebec, Canada.

Responsibilities

Build conversational AI systems that understand customer needs, take action, and escalate when appropriate.; Develop agent memory, knowledge graph, and customization tools to help systems adapt to different workflows.; Train, evaluate, and deploy models for prediction, classification, ranking, and capacity forecasting.; Create offline and online evaluations, simulations, and CI checks to detect regressions and measure production quality.; Build shared ML and LLM infrastructure for model serving, evaluation, observability, and product development.; Monitor and maintain production models and agents, and improve them based on measured results.; Contribute to technical reviews, documentation, and mentorship across engineering teams.

Requirements

At least 6 years of software or machine learning engineering experience, including a track record of shipping and operating ML or LLM systems in production.; Strong Python skills and software engineering fundamentals, including writing reliable, maintainable, and testable code.; Experience building conversational AI for phone or SMS, or related real-time and streaming systems such as voice, SIP, or WebRTC.; Hands-on experience training and deploying ML models, or building LLM systems with prompting, tool use, agents, or retrieval.; Experience with evaluation frameworks, simulations, production monitoring, or ML platform tooling.; Familiarity with tools such as Ray Serve, Dagster, LiveKit, Vertex AI, GCP, Kubernetes, Pulumi, and AI provider APIs.; Comfort taking ownership from problem definition through production, working with ambiguity, and supporting teammates through reviews and mentorship.