Senior Machine Learning Engineer
Build and operate customer-facing machine learning systems, taking problems from rapid experimentation through production while balancing model quality, inference cost, and reliability at scale.
Join a product and platform team building conversational AI for phone and SMS interactions. You will take machine learning and LLM features from prototype through production, helping voice agents respond to customers, take action, and escalate when needed.
Build conversational AI systems that understand customer needs and take appropriate actions.
Develop agent tooling, including memory systems, knowledge graphs, and customization frameworks.
Train, evaluate, and deploy models for prediction, classification, ranking, and capacity forecasting using Ray Serve and Dagster.
Create offline and online evaluations, simulations, and CI checks to detect regressions and measure production quality.
Build shared ML and LLM infrastructure, including serving, evaluation, and observability tools.
Contribute to design and code reviews, technical writing, and mentorship.
Monitor and maintain models and agents in production.
At least 6 years of software or machine learning engineering experience, including a track record of shipping ML or LLM systems to production.
Strong Python skills and software engineering fundamentals.
Hands-on experience training and deploying ML models, or building with LLMs using prompting, tools, agents, or retrieval.
Experience with evaluation frameworks, production monitoring, model serving, or related ML platform tooling.
Familiarity with Ray Serve, Dagster, LiveKit, Vertex AI, GCP, Kubernetes, Pulumi, or AI provider APIs.
Experience with real-time or streaming systems such as voice, telephony, SIP, WebRTC, or low-latency inference is valuable.
Comfort working autonomously, navigating ambiguity, and collaborating in a startup or high-growth environment.
Salary range: CAD $200,000 to $250,000 annually. Equity and visa sponsorship details were not provided.
On-site in Montreal, Quebec, Canada.
Build and operate customer-facing machine learning systems, taking problems from rapid experimentation through production while balancing model quality, inference cost, and reliability at scale.
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