Remote job
Senior/Staff Machine Learning Engineer
Job details
About this role
Role overview This is a senior-level engineering position on a data science team that powers a high-volume digital advertising platform. The work centers on building and improving the machine learning infrastructure that supports real-time ad optimization at very large scale. The role spans both individual technical contribution and collaboration with data scientists, backend engineers, and engineering leadership.
Responsibilities - Design modular, scalable real-time data pipelines capable of handling massive datasets. - Propose and coordinate architectural improvements to large-scale ML data pipelines. - Implement custom machine learning algorithms optimized for low-latency environments. - Build and maintain microservice architectures that train, serve, and monitor thousands of ML models concurrently. - Translate ambiguous research and product questions into concrete, actionable engineering plans.
Requirements - Demonstrated ability to take loosely defined problems and break them into structured work. - Track record of shepherding complex technical projects to completion independently and through coordination. - Deep understanding of algorithms, software design, concurrency, and data structures. - Hands-on experience implementing probabilistic or machine learning algorithms. - Experience designing scalable distributed systems. - Strong academic background in computer science or equivalent experience in a competitive technology environment.
Nice to have - Experience working in collaborative, friendly engineering cultures.
Benefits and work setup - Remote-first position open to candidates located anywhere in Canada. - Base salary band of CAD $170,400 to $234,300, plus potential eligibility for bonuses, equity, or commissions depending on location and role. - Retirement or pension savings plan, competitive paid time off, mental health support, and health benefits from day one. - Home office reimbursements, optional co-working access, parental leave, learning budgets, and structured onboarding.