Senior Machine Learning Engineer, Developer Advocacy

As a Senior Machine Learning Engineer, Developer Advocacy, you will take ownership of building and improving intelligent recommendation systems that help users achieve better outcomes. You will combine machine learning expertise, product thinking, and strong communication skills to deliver scalable solutions and measurable improvements.

• Lead the evolution of personalized recommendation systems by developing ranking, sequencing, candidate selection, and next-best-action approaches.
• Build, deploy, monitor, and continuously improve machine learning models powering recommendation experiences.
• Own model development workflows, including training, validation, versioning, deployment, and performance monitoring.
• Define recommendation quality metrics and establish evaluation frameworks across offline, online, and longitudinal measurements.
• Develop and maintain feature pipelines, monitoring strategies, and supporting model infrastructure.
• Deliver incremental improvements by leveraging existing data and infrastructure while identifying opportunities for future enhancements.
• Collaborate with software engineers to productionize machine learning models and integrate them safely into product systems.
• Partner with analytics teams on instrumentation, data quality, dashboards, experimentation, and performance analysis.
• Work closely with product, engineering, developer-focused, documentation, and go-to-market teams to translate business needs into measurable hypotheses.
• Communicate modeling decisions, trade-offs, uncertainty, and results clearly to both technical and non-technical stakeholders.
• Contribute to responsible AI practices, including privacy, fairness, explainability, and transparent personalization.

Requirements:

The ideal candidate is an experienced machine learning engineer with strong expertise in recommendation systems, applied modeling, and product-focused data science. You should be comfortable owning models throughout their lifecycle and collaborating with engineering and business teams to deliver impactful solutions.

• Proven experience building recommendation, ranking, search, matching, propensity, or next-best-action systems.
• Strong understanding of personalization techniques and the ability to start with simple, explainable approaches when appropriate.
• Experience independently building, validating, monitoring, and improving machine learning models used in production environments.
• Ability to work effectively within version-controlled codebases and collaborate with software engineering teams.
• Experience with distributed systems and technologies such as HTTP, gRPC, streaming architectures, Go, or TypeScript is beneficial.
• Strong product mindset with the ability to turn ambiguous goals into measurable experiments and iterative improvements.
• Experience working with behavioral data, SaaS telemetry, customer data, or large-scale analytics environments.
• Strong analytical, problem-solving, and communication skills.
• Ability to explain technical concepts, modeling choices, and results clearly to diverse audiences.
• Experience with content recommendation, education platforms, onboarding systems, or learning experiences is a plus.
• Familiarity with directed graphs, sequence models, contextual bandits, or exploration strategies is beneficial.
• Experience with open-source software or transparent development practices is preferred.
• Knowledge of privacy, fairness, responsible AI, and explainable personalization principles is an advantage.

Benefits:

• Competitive base compensation range of $164,490 CAD – $197,389 CAD, depending on experience, skills, and level.
• Equity opportunities through Restricted Stock Units (RSUs), providing ownership in company success.
• Fully remote work environment with global collaboration opportunities.
• Opportunity to work on impactful machine learning products used at large scale.
• High-growth environment with autonomy, trust, and opportunities to innovate.
• Open and transparent culture focused on collaboration and meaningful outcomes.
• Career development opportunities with clear growth pathways.
• Supportive and accessible leadership team.
• Annual leave policy offering up to 30 days per year, including dedicated company shutdown days.
• In-person onboarding experience to help new team members integrate successfully.
• Opportunity to contribute to open-source initiatives and community-driven technology.