Senior Machine Learning Engineer

Summary

Join Leonardo.Ai's growing global R&D team as a Senior Machine Learning Engineer and contribute to building one of the world’s highest-throughput Generative AI platforms. You will work at the intersection of Generative AI and MLOps, collaborating with researchers and engineers to bring new models from prototype to production. This role involves building and maintaining production pipelines for generative models, optimizing inference for speed and efficiency, and developing automated MLOps pipelines. You will also design scalable data pipelines and infrastructure on AWS, and be embedded in other teams to support high-impact projects. Your work will directly impact the delivery of AI-powered features, making creativity accessible to millions. The position offers a flexible work environment and opportunities for professional development.

Requirements

• Experience deploying diffusion-based or similar generative models into production and working with inference optimisation techniques
• Skilled in building automated, reliable workflows for training, deploying, and monitoring ML models at scale
• Hands-on with AWS (S3, EC2, SageMaker), Kubernetes, Docker, and Infrastructure-as-Code (Terraform)
• Proficient in techniques such as quantisation, distillation, caching, and distributed inference
• Ability to design scalable data pipelines and storage solutions (SQL/NoSQL)
• Strong Python skills and a focus on writing clear, maintainable, and collaborative code
• Thrive in cross-functional teams, value open feedback, and enjoy supporting others’ success while learning continuously

Responsibilities

• Collaborate with researchers to bring new models from prototype to production and ensure they deliver meaningful value
• Build and maintain production pipelines for diffusion-based and related generative models (e.g. LoRA, ControlNet)
• Optimise inference for speed, reliability, and efficiency using techniques such as quantisation, distillation, caching, and multi-GPU parallelism
• Tackle complex challenges like orchestrating multi-GPU video pipelines while ensuring systems are intuitive and maintainable
• Develop and maintain automated MLOps pipelines covering training, deployment, monitoring, and retraining
• Build CI/CD workflows for machine learning that make handovers from research to production seamless and safe
• Create scalable data pipelines and storage solutions to support high-throughput workloads
• Set up clear monitoring and alerting for model performance (e.g. Prometheus, Grafana, CloudWatch)
• Design secure, reliable infrastructure on AWS (S3, EC2, SageMaker) using Infrastructure-as-Code tools like Terraform
• Be embedded in other teams, from Generations to Enterprise, to support high-impact projects requiring deep ML expertise
• Develop shared tooling, reusable workflows, and architecture patterns that help teams across Leonardo build and ship faster
• Promote knowledge-sharing, best practices, and scalable solutions across the organisation

Benefits

• Reward package including equity
• 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