Director of Engineering, AI

Accountabilities

• Lead the AI engineering organization and build high-functioning teams of AI and Machine Learning engineers and applied scientists focused on delivering high-quality results.
• Establish and execute a platform-wide AI and Machine Learning strategy aligned with product priorities, customer needs, and long-term business objectives.
• Lead teams responsible for developing foundational AI and Machine Learning products and capabilities that can be leveraged across the broader product portfolio.
• Partner with senior engineering leadership to define organizational strategy, establish new teams, develop sustainable team structures, and guide architecture and product direction.
• Lead the development of highly scalable, reliable, and distributed AI products while ensuring strong engineering practices and consistent execution.
• Guide teams through complex technical and product roadmaps, working closely with Product Management and Design to align strategy, priorities, and delivery.
• Provide technical leadership across architecture, system design, engineering execution, AI product quality, and platform scalability.
• Develop and maintain systems for deploying, managing, evaluating, and serving Machine Learning models and agentic products at scale.
• Drive effective evaluation, quality, and model judgment practices across research-oriented AI initiatives and product engineering.
• Recruit, coach, develop, and retain engineering leaders, applied scientists, and AI/ML engineers while establishing clear career paths and performance expectations.
• Provide regular feedback and coaching to leaders and support performance reviews, career development, and organizational planning.
• Partner across Engineering, Product, Design, Data Science, Infrastructure, Go-to-Market, Legal, and Support to remove organizational barriers and improve execution.
• Participate in architectural decisions, quarterly planning, staffing strategy, roadmap development, and major product launches.
• Balance technical debt, product features, bugs, and business priorities while keeping teams focused on the highest-impact opportunities.
• Represent the organization as a subject matter expert in AI and Machine Learning, bringing relevant industry trends and emerging technologies into strategy and roadmap discussions.

Requirements

• 7+ years of experience building and supporting products in production environments.
• 7+ years of software development experience, including several years leading engineering teams and managing through other leaders.
• Demonstrated success building sustainable, high-performing engineering organizations through hiring, coaching, organizational design, and leadership development.
• Proven experience leading engineering teams that build Artificial Intelligence and Machine Learning or agentic products, including collaboration with or leadership of applied scientists and AI/ML engineers.
• Recent, direct experience, typically within the last 2–3 years, leading or closely driving the development of modern AI products, agentic systems, or Large Language Model (LLM)-based products built on foundation models.
• Strong understanding of AI product evaluation, quality, model judgment, and the challenges of translating research-oriented work into production-grade products.
• Demonstrated ability to lead mixed teams of applied scientists and AI/ML engineers while supporting research-informed development and product engineering.
• Experience scaling high-performance teams through the hiring and development of engineering leaders, applied scientists, and AI/ML engineers.
• Experience within a Software-as-a-Service (SaaS) or product organization that has undergone a significant Artificial Intelligence transformation or shift in product direction is preferred.
• Strong ability to establish a clear vision and align teams toward high-impact work across complex product portfolios serving diverse, global customers.
• Proven ability to influence and align stakeholders across different functions and perspectives to drive successful outcomes.
• Experience developing enterprise-scale distributed applications using technologies such as Python, Java, and distributed queuing systems.
• Experience developing and scaling systems for Machine Learning model deployment, management, evaluation, and serving.
• Experience with generative AI, including open-source and third-party solutions such as OpenAI.
• Strong strategic thinking, communication, collaboration, and executive-level stakeholder management skills.
• Pragmatic, adaptable, highly motivating leadership style with the ability to guide teams through ambiguity and rapidly changing AI technologies.

Benefits

• Current hiring range of CA$232,000–CA$300,000 annually.
• Broader base pay range of CA$232,000–CA$348,000, with individual compensation determined by geographic zone, relevant experience, skills, performance, and impact.
• 100% employer-paid health benefits for eligible employees and dependents, including medical, dental, vision, life insurance, and disability coverage.
• 25 days of annual vacation, plus 5 paid sick days, public holidays, and additional company-wide Rest & Recharge days.
• Free access to mental health support, including coaching, therapy sessions, and digital wellness resources.
• CA$950 annual Lifestyle Spending Account supporting physical, mental, and financial well-being.
• One-time US$550 equivalent home-office setup stipend.
• Monthly US$50 equivalent internet stipend.
• Confidential, no-cost access to financial experts for personalized financial wellness support.
• Subsidized childcare and eldercare options.
• Employer matching for eligible charitable donations.
• Remote work opportunity for candidates based in Alberta, British Columbia, or Ontario.
• Opportunity to lead AI and Machine Learning strategy at significant product and organizational scale.
• High-impact leadership scope spanning engineering, product, design, data science, infrastructure, and other functions.
• Opportunities to shape AI capabilities, technical strategy, organizational growth, and long-term product direction.