Distributed AI Systems Engineer – Multi-GPU & Benchmarking

Luxoft; Polska; On-site; PLN 180,000 - 260,000; Full time

Benefits offered by this job

Laptop

Monitor

Job summary

AMD is seeking a software engineer focused on Distributed Inferencing on AMD GPUs to enhance performance of key applications and benchmarks. You will join a core team exploring the latest hardware and software tech, driving scalable AI workloads across multi-GPU and multi-node clusters.

You will own parallelization strategies, contribute to model infrastructure, and build real-time dashboards for performance and reliability.

Qualifications

• Hands-on experience with AI inference or serving frameworks such as vLLM, SGLang, and Llama.cpp.
• Understanding KV cache transfer mechanisms and expert parallelization approaches.
• Strong C/C++ and Python skills, with software design, debugging, performance analysis, and test development.
• Experience running AI workloads on large-scale, heterogeneous clusters.
• Familiarity with cluster management/orchestration platforms like SLURM and Kubernetes.
• Experience with GitHub, Jenkins, and modern CI/CD workflows.

Responsibilities

• Distributed AI enablement and benchmarking on large-scale distributed systems to evaluate performance and scalability.
• Optimize AI workloads across scale-up, scale-out, and distributed configurations.
• Collaborate with GPU library teams to analyze and optimize distributed workloads for throughput and latency.
• Develop and apply parallelization strategies for AI workloads across diverse systems.
• Contribute to distributed model management, zoos, monitoring pipelines, and documentation.
• Build real-time dashboards reporting performance, accuracy, and reliability metrics.

Skills

C++/Python

AI Inference Frameworks

KV Cache & Parallelization

Distributed AI Systems

Cluster Orchestration (SLURM/K8s)

CI/CD Tools

Development Workflows

Tools

GitHub

Jenkins

Kubernetes

Job description

AMD is seeking a software engineer focused on Distributed Inferencing on AMD GPUs to enhance performance of key applications and benchmarks. You will join a core team exploring the latest hardware and software tech, driving scalable AI workloads across multi-GPU and multi-node clusters.

You will own parallelization strategies, contribute to model infrastructure, and build real-time dashboards for performance and reliability.