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
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Design and implement distributed training pipelines handling high-volume data and complex model architectures
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Develop low-latency inference systems providing real-time, high-accuracy predictions in production
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Optimise and extend machine learning frameworks to improve training and inference performance
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Leverage GPU programming (CUDA, cuDNN, TensorRT) to maximise efficiency
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Automate model experimentation, tuning and retraining in partnership with research teams
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Work with infrastructure specialists to optimise workflows and reduce compute costs
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Assess and integrate emerging open-source tools to enhance ML development and deployment
Skills and Experience
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5+ years’ experience in machine learning with a focus on training and inference systems
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Strong programming expertise in Python and C++ or CUDA
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Proficiency with PyTorch, TensorFlow or JAX
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Hands-on experience with GPU acceleration and distributed training (Horovod, NCCL or similar)
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Background in real-time, low-latency ML pipelines
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Familiarity with cloud and orchestration technologies
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Contributions to open-source ML or distributed systems projects are advantageous
What’s on Offer
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Work on technically demanding, high-impact ML systems at global scale
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Collaborate with a multidisciplinary team of experts in research, engineering and HPC
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Join a culture that prizes innovation, precision and technical excellence
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Competitive remuneration, benefits and clear scope for progression
