Machine Learning Team Lead

Leonardo.Ai seeks a Machine Learning Engineering Team Lead to drive and scale our AI infrastructure with empathy, collaboration, and impact.

At Leonardo.Ai, we are advancing our generative AI platform to empower millions, regardless of expertise, with intuitive tools for creating high-quality images and videos. Now part of the Canva family, we're ready to build a world-class R&D team that seamlessly integrates AI products, tools, and features, making creativity limitless for nearly a quarter of a billion users.

The Role

As a Machine Learning Engineering Team Lead, you will lead our MLOps efforts, leading a diverse and talented team while fostering an inclusive and supportive environment. You will be instrumental in bridging the gap between research and production, ensuring our AI models are deployed and optimised with excellence. Your leadership will shape best practices, nurture innovation, and drive impactful AI initiatives that elevate Leonardo’s AI capabilities.

What You'll Do

Empowering Leadership & Growth

• Inspire, mentor, and develop a high-performing team of ML Engineers and MLOps specialists, cultivating an environment of trust and collaboration.

• Drive continuous learning and professional growth through mentorship, knowledge-sharing, and structured development opportunities.

• Lead empathetically, ensuring every team member is supported and empowered to do their best work.

• Champion diversity of thought and inclusive decision-making, ensuring a broad range of perspectives contribute to innovation.

Strategic AI & MLOps Leadership

• Define and implement best-in-class MLOps infrastructure and AI deployment strategies that are scalable, efficient, and adaptable.

• Partner with research scientists, software engineers, and product teams to align AI strategies with user needs and business objectives.

• Stay ahead of technological trends, guiding the team in adopting cutting-edge tools and methodologies.

Building Scalable & Responsible AI Systems

• Design and maintain robust ML pipelines, covering data ingestion, model training, deployment, monitoring, and retraining.

• Develop reusable frameworks to accelerate experimentation, ensuring smooth iteration and continuous improvement.

• Integrate workflow automation tools, optimising for performance, sustainability, and long-term scalability.

Cloud & DevOps Integration

• Oversee cloud infrastructure implementation and management, primarily on AWS, leveraging tools like Terraform for infrastructure automation.

• Build robust CI/CD pipelines designed for seamless machine learning workflows, ensuring reliability and efficiency.

• Champion responsible AI deployment, optimising resource allocation and managing cloud costs with transparency.

Data Engineering & Responsible AI

• Develop scalable ETL pipelines to process and store large datasets efficiently, ensuring ethical AI practices in data handling.

• Automate data ingestion and transformation workflows, maintaining integrity, security, and compliance.

• Enhance data accessibility and usability for research and product teams, fostering collaborative AI development.

Model Deployment & Monitoring

• Lead deployment of machine learning models with a focus on resilience, fairness, and real-world impact.

• Implement monitoring solutions that proactively track model performance, detect drift, and enable responsible retraining.

• Apply techniques such as model quantisation, distillation, and caching to enhance efficiency and sustainability.

Skills We Value

• Proven experience leading and mentoring technical teams with a people-first approach.

• Strong expertise in building and managing MLOps pipelines using frameworks such as Kubeflow or MLflow.

• Proficiency in Python, with a commitment to writing clean, maintainable, and well-documented code.

• Hands-on experience with AWS cloud services and infrastructure-as-code tools like Terraform.

• Deep understanding of Docker, Kubernetes, and scalable container orchestration.

• Knowledge of CI/CD principles, ensuring rapid and reliable machine learning model deployment.

• Strong background in data engineering, including designing scalable ETL pipelines and working with SQL/NoSQL databases.

• Familiarity with monitoring tools such as Prometheus, Grafana, or CloudWatch.

Nice-to-Have Skills

• Experience with distributed computing frameworks such as Apache Spark, Dask, or Ray.

• Understanding of network configurations and security best practices for AI deployments.

• Hands-on experience with API integrations, model explainability, and performance optimisation techniques.

• Passion for fostering an inclusive, innovative, and high-impact AI environment.

Join us at Leonardo.Ai and help shape the future of AI in a way that is not just powerful but also thoughtful, inclusive, and human-centred!

What's in it for you?

A range of benefits to set you up for every success in and outside of work. Here's a taste of what's on offer:

• Impact the future of AI

• Reward package including equity - we want our success to be yours too

• Inclusive parental leave policy that supports all parents & carers with 18 weeks paid leave

• An annual Vibe & Thrive allowance to support your wellbeing, social connection, office setup & more

• Flexible leave options that empower you to be a force for good, take time to recharge and support you personally, including remote working abroad

• Support with your professional development

• Fun and engaging company events, both virtual and in-person

• 20 days annual leave

• Novated car leasing

We're committed to building a diverse, safe and inclusive environment where employees can be authentic and teams collaborate effectively to bring innovative ideas to life.