Build high-performance machine learning infrastructure for large-scale training and real-time inference, leveraging distributed computing, GPU acceleration, advanced frameworks, and scalable production systems.

What You'll Do:

1. You’ll build the systems that power the training and deployment of sophisticated machine learning models at scale. Working alongside researchers, hardware specialists, and software engineers, you’ll tackle challenging problems across distributed computing, GPU acceleration, model performance, and real-time inference.
2. Your work will accelerate experimentation, improve training efficiency, and enable high-performance models to deliver accurate predictions within demanding production environments.

Your responsibilities will include:

1. Develop large-scale distributed training pipelines for complex datasets and machine learning models.
2. Build and optimise low-latency inference pipelines for real-time production predictions.
3. Develop high-performance libraries that enhance machine learning frameworks.
4. Maximise training and inference performance through GPU hardware and acceleration technologies.
5. Design scalable model frameworks capable of processing high-volume datasets.
6. Automate machine learning experimentation, hyperparameter tuning, and model retraining.
7. Partner with high-performance computing specialists to optimise workflows and reduce training costs.
8. Evaluate and deploy third-party tools that improve model development, training, and inference.
9. Investigate the internals of open-source ML frameworks and extend their functionality.
10. Identify performance bottlenecks and implement solutions to improve scalability and efficiency.
11. Collaborate across engineering and research teams to deliver robust production ML systems.

Why Join Us:

1. This is an opportunity to work on technically demanding problems at the intersection of machine learning, distributed computing, and high-performance hardware.
2. You’ll work with advanced GPU technologies and large-scale infrastructure while contributing to systems that significantly accelerate research and experimentation.
3. The role offers the chance to work closely with researchers and infrastructure specialists, explore the internals of leading open-source frameworks, and develop solutions that push the boundaries of ML performance.
4. Rather than simply applying existing tools, you’ll have the opportunity toimprove, extend, and optimise the technology itself .

About You:

1. You’re an experienced Machine Learning Engineer with5+ years of experience focused on machine learning training systems, inference systems, or ML infrastructure.
2. You have strong software engineering capabilities inPython, CUDA, or C++ , along with experience using modern frameworks such asPyTorch, TensorFlow, or JAX .
3. You understand GPU programming and acceleration technologies such asCuDNN and TensorRT , and ideally have experience with distributed training technologies includingHorovod or NCCL .
4. Experience with real-time, low-latency ML systems, cloud platforms, orchestration tools, or open-source contributions is highly valued.
5. Above all, you’re curious about how systems work beneath the surface, enjoy solving complex performance challenges, and are motivated to build ML infrastructure that operates efficiently at scale.

#MachineLearning #MLEngineer #ArtificialIntelligence #GPUComputing #CUDA #CPlusPlus #Python #DistributedSystems #DistributedTraining #PyTorch #TensorFlow #JAX #LowLatency #MLInfrastructure #HighPerformanceComputing