SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Revolut is a fintech super app serving 80+ million customers globally with products spanning spending, saving, investing, exchanging, and travel. The company has 13,000+ employees across offices and remote locations.
The Technology team builds the systems and infrastructure powering Revolut's innovative platform. You will join the AI team as a Python Engineer focused on developing core MLOps infrastructure that enables scientists and engineers to build and deploy machine learning solutions at scale.
In this role, you will:
• Design, build, and maintain scalable backend services and tooling supporting the AI lifecycle and GPU-based workloads
• Profile and optimize model training and inference workloads (latency, throughput, stability) using PyTorch and CUDA-enabled libraries
• Improve GPU compute utilization, memory consumption, and bandwidth across shared, multi-tenant, and multi-node environments
• Implement mixed-precision execution, quantization, efficient batching, gradient accumulation, and memory-efficient attention mechanisms
• Create repeatable benchmarking practices, automated regression tests, and guide engineering teams in selecting execution frameworks and GPU types
You will write high-quality code and build automated solutions for heavily regulated financial systems, bringing sharp thinking and a focus on meaningful impact.
REQUIREMENTS:
• Proven track record designing and operating scalable backend systems in production (Linux, Kubernetes, containerized workloads) using Python
• Experience running, profiling, and optimizing machine learning workloads built with PyTorch or equivalent on NVIDIA GPUs
• Solid understanding of GPU execution concepts: memory hierarchy, CPU/GPU synchronization, host-to-device transfers, CUDA stream behavior
• Familiarity with multi-GPU communication (NCCL), data/tensor parallelism, and resource allocation in Kubernetes
• Degree in STEM subject (or equivalent) with strong foundation in computer science principles, testing, observability, and operational ownership
