Lead Machine Learning Engineer

Saris•Toronto, ON

About The Position

Our core engineering team is looking for a hands-on ML Engineering Lead who thrives in early-stage, ambiguous environments. You’ve led ML systems from v1 to scale, and enjoy defining both the technical direction and the systems that power them. Your mission is to Own and lead the ML/AI function end-to-end, setting technical direction and standards across the company Architect and guide the development of multi-modal, agentic AI systems powering real-world workflows Define and oversee evaluation frameworks, datasets, and performance metrics to continuously improve agent quality Drive productionization of ML systems, ensuring reliability, scalability, and compliance in real-world environments Build and mentor a high-performing ML team over time, setting best practices across modeling, experimentation, and deployment

Requirements

8+ years of experience in ML/AI engineering, including time as a technical lead or manager
Proven track record of leading ML initiatives end-to-end, from problem definition → production deployment
Deep experience with LLMs and/or agentic systems, ideally in real-world, customer-facing applications
Strong understanding of ML fundamentals (deep learning, transformers, model evaluation, tradeoffs)
Experience scaling ML systems in production, including monitoring, iteration, and reliability
Demonstrated ability to lead engineers, influence architecture decisions, and drive technical direction
Comfortable operating in early-stage, ambiguous environments with high ownership
Strong communication skills with the ability to translate complex ML concepts into clear decisions

Nice To Haves

Experience building agentic systems, orchestration layers, or long-context reasoning systems
Comfortable across the stack (data → modeling → infra → APIs)
Worked with both open-source and closed LLMs, including fine-tuning or retrieval systems (RAG)
Strong product mindset and care deeply about real-world impact, not just model performance

Responsibilities

Own and lead the ML/AI function end-to-end, setting technical direction and standards across the company
Architect and guide the development of multi-modal, agentic AI systems powering real-world workflows
Define and oversee evaluation frameworks, datasets, and performance metrics to continuously improve agent quality
Drive productionization of ML systems, ensuring reliability, scalability, and compliance in real-world environments
Build and mentor a high-performing ML team over time, setting best practices across modeling, experimentation, and deployment