Design AI - ML Engineering Manager (Remote AU/NZ)

As a Design AI - ML Engineering Manager at Canva, your primary responsibilities include building systems for design generation, coaching ML engineers, and collaborating across teams to align on product direction. You'll focus on both technical leadership and nurturing your team's growth while balancing innovation with project delivery expectations. Additionally, you'll be involved in architectural decisions that push the boundaries of design technology.

To qualify for the Design AI - ML Engineering Manager position at Canva, you should have previous experience as an ML Engineer or in leading ML product teams. A proven track record in managing long-term engineering projects within the ML space is crucial. Strong technical and people leadership skills, as well as a genuine interest in design, are essential for success in this role.

A typical day for a Design AI - ML Engineering Manager at Canva involves coordinating with teams of engineers, product managers, and researchers. You might spend your time guiding architectural decisions, facilitating knowledge sharing sessions, engaging in project planning, and ensuring technical excellence. The role blends hands-on engineering tasks with leadership responsibilities, focusing on fostering team growth and innovation.

As a Design AI - ML Engineering Manager at Canva, you'll work on ambitious projects focused on generative design systems. This includes developing cutting-edge techniques that allow users to create and edit designs easily. Collaborating with diverse teams, you'll tackle exciting challenges that involve translating the latest AI research into practical applications that enhance user experience.

Canva offers a comprehensive benefits package for its Design AI - ML Engineering Manager. This includes equity packages to ensure you share in the company's success, flexible leave options, and an inclusive parental leave policy. Additionally, you’ll also benefit from an annual Vibe & Thrive allowance aimed at supporting personal wellbeing, professional growth, and maintaining a positive work-life balance.

In prioritizing technical projects, it's crucial to align project goals with business objectives while considering resource availability. Start by identifying the impact of each project and the necessary skills, then consult with your team and stakeholders to assess feasibility and urgency. Balancing exploratory work with pressing delivery needs will ensure that your approach drives innovation while meeting critical deadlines.

Mentoring ML engineers is about fostering their technical growth and confidence. Share your expertise through regular knowledge-sharing sessions, promote collaboration across projects, and encourage open communication. Recognizing individual strengths and providing tailored guidance helps cultivate a skilled team while fostering an environment where engineers feel supported and empowered to take on challenges.

To stay current with advancements in ML and AI, dedicate time to reading research papers, attending industry conferences, and following thought leaders on social media. Encourage your team to engage in continuous learning by promoting collaborative discussions on new techniques and best practices. Subscription to relevant journals and joining online courses can also provide vital insights into the evolving technology landscape.

Effective resource planning begins with understanding the strengths and skills of each team member. Evaluate project requirements and match them with individuals who possess the relevant expertise. Regularly review team capacities, monitor workload, and be flexible in reallocating resources as needed to address project demands without straining any team members.

Incorporating feedback from product managers requires a collaborative mindset. Establish regular check-ins and open lines of communication with product managers, ensuring their insights are fully understood and considered in the engineering processes. Engage in joint planning sessions to align engineering efforts with product roadmaps, allowing the team to make decisions that reflect both user needs and technical goals.

Measuring the success of your team in ML projects involves a mix of quantitative and qualitative metrics. Track key performance indicators—such as project delivery timelines, user engagement, and system performance—while also valuing team collaboration, creativity, and satisfaction. Regularly solicit feedback from team members to refine your understanding of success and adapt your goals accordingly.

Providing a detailed account of a successful ML project showcases your leadership abilities. Describe the project's objectives, challenges faced, and the strategies you implemented to achieve results. Highlight your role in managing the team and resources, as well as how the final product positively impacted users, underscoring your effectiveness as a leader in the ML space.

Fostering a culture of innovation starts with creating an open and supportive environment. Encourage team members to propose new ideas and explore experimental projects outside of regular work. Establish 'innovation sprints' where the team can dedicate time to brainstorming and testing novel concepts, ensuring they feel empowered and valued for their contributions to the innovative culture.

Anticipating challenges is a key aspect of strategic leadership. In this role, you might face challenges like balancing the pace of innovation with delivering high-quality features, ensuring your team remains agile and adaptable amidst rapid technological changes. Addressing communication gaps between technical and non-technical stakeholders can also present challenges, so building strong relationships is vital for success.

To keep your team engaged and motivated, foster a culture of recognition by celebrating achievements, both big and small. Create opportunities for personal and professional growth through training programs and mentorship. Actively listen to your team's concerns, solicit their feedback on processes, and involve them in decision-making to cultivate a sense of ownership and commitment to their work.