Machine Learning Engineer - ScalingDo you want to help transform an industry by applying cutting-edge technology to solve meaningful, real-world problems? Join an innovative, forward-thinking team building impactful solutions in a fast-paced and collaborative environment.As a Machine Learning Engineer - Scaling, you’ll play a critical role in developing and optimising advanced machine learning systems to address complex challenges. You will work closely with cross-disciplinary teams to productionise model workflows, explore innovative methods, and contribute to scalable and efficient AI applications. This is an exciting opportunity for individuals who thrive on technical challenges, value ownership, and are motivated by innovation and impact.Responsibilities:Build and maintain scalable training and inference pipelines for modern AI models (e.g. Transformers, Sequence Models, etc.).Optimise model performance, ensuring low latency and high throughput in various operational environments.Design and implement reusable, modular machine learning components for internal or broader use.Collaborate with researchers to transition experimental code into fully operational, production-grade systems.Manage and enhance machine learning infrastructure—including data pipelines, distributed computing, and experiment tracking tools.Requirements:Essential Qualifications:MSc or PhD in fields such as Machine Learning, Computer Science, Applied Mathematics, or closely related areas.Strong programming expertise in Python, with proficiency in libraries like PyTorch, JAX, or TensorFlow.Hands-on experience developing and scaling machine learning pipelines for production environments.Familiarity with MLOps practices and tools such as Weights & Biases, Ray, or Docker.In-depth understanding of modern AI architectures, including Transformers, Diffusion Models, or similar frameworks.A proactive attitude, with the ability to thrive in a high-speed environment and adapt to ambiguity.Bonus Points:Contributions to open-source machine learning projects or tools.Experience in distributed training, model compression, or large-scale model serving.Expertise in scaling AI systems for large post-training workloads.Experience integrating machine learning systems into user-facing applications or APIs.An interest in applying machine learning in fields like biology, healthcare, or other specialised domains (prior experience not required but advantageous).What We’re Looking For:If you are excited by complex and deeply technical challenges, enjoy working in a collaborative and fast-paced team environment, and want to make a real impact in a rapidly growing sector, this role is for you. We value innovation, ownership, and enthusiasm for tackling industry-defining problems.