Machine Learning Engineer

Machine Learning Engineer - Large-Scale Systems & Real-Time Inference

An opportunity for a Machine Learning Engineer to design and deliver large-scale systems supporting the training, optimisation and deployment of advanced models in high-performance environments. You will collaborate with researchers, software engineers and hardware specialists to build solutions that push the limits of GPU acceleration, distributed computing and automated experimentation.

Key Responsibilities

• Design and implement distributed training pipelines handling high-volume data and complex model architectures

• Develop low-latency inference systems providing real-time, high-accuracy predictions in production

• Optimise and extend machine learning frameworks to improve training and inference performance

• Leverage GPU programming (CUDA, cuDNN, TensorRT) to maximise efficiency

• Automate model experimentation, tuning and retraining in partnership with research teams

• Work with infrastructure specialists to optimise workflows and reduce compute costs

• Assess and integrate emerging open-source tools to enhance ML development and deployment

Skills and Experience

• 5+ years' experience in machine learning with a focus on training and inference systems

• Strong programming expertise in Python and C++ or CUDA

• Proficiency with PyTorch, TensorFlow or JAX

• Hands-on experience with GPU acceleration and distributed training (Horovod, NCCL or similar)

• Background in real-time, low-latency ML pipelines

• Familiarity with cloud and orchestration technologies

• Contributions to open-source ML or distributed systems projects are advantageous

What's on Offer

• Work on technically demanding, high-impact ML systems at global scale

• Collaborate with a multidisciplinary team of experts in research, engineering and HPC

• Join a culture that prizes innovation, precision and technical excellence

• Competitive remuneration, benefits and clear scope for progression