Senior Software Engineer (AI Systems Performance)

Luxoft; Polska; On-site; PLN 180,000 - 240,000; Full time

Job summary

Luxoft is seeking a software engineer to advance distributed AI on GPUs, improving performance of key applications and benchmarks. You will join a core team of specialists and work with the very latest hardware and software technologies.

Ideal candidates have strong C++/Python skills, experience with multi-GPU/multi-node clusters, and familiarity with CI/CD, SLURM, Kubernetes, and benchmarking automation.

Qualifications

• Experience with C/C++ and Python; performance analysis and testing.
• Experience running AI workloads on large-scale clusters.
• Familiarity with SLURM and Kubernetes for orchestration.
• Ability to develop benchmarks and automation in AI/ML.

Responsibilities

• Enable and benchmark AI models on large-scale distributed systems.
• Optimize AI workloads across multi-GPU and multi-node configurations.
• Collaborate with GPU library teams to optimize distributed workloads.
• Develop parallelization strategies for AI workloads.
• Contribute to model management, benchmarking pipelines, and docs.
• Build real-time dashboards for performance metrics.

Skills

AI frameworks

KV cache

C/C++ and Python

Distributed AI workloads

Cluster orchestration

CI/CD tooling

Education

Master’s or PhD in CS

Tools

GitHub

Jenkins

Job description

Project description

A leading semiconductor and high-performance computing company is looking for a software engineer who is passionate about Distributed Inferencing on GPUs and improving the performance of key applications and benchmarks. You will be a member of a core team of incredibly talented industry specialists and will work with the very latest hardware and software technology

Responsibilities

• We are seeking a software engineer with strong technical expertise in C++/ Python development, solving performance and investigating scalability on multi-GPU, multi-node clusters. He is also passionate about quality assurance, benchmarking, and automation in the AI/ML space. The ideal candidate thrives in both collaborative and independent environments, demonstrates excellent problem-solving skills, and takes ownership in defining goals and delivering impactful solutions.KEY RESPONSIBILITIES:Distributed AI Enablement and Benchmarking: Enable and benchmark AI models on large-scale distributed systems to evaluate performance, accuracy, and scalability.Scalable Systems Optimization: Optimize AI workloads across scale-up (multi-GPU), scale-out (multi-node), and scale-across distributed system configurations.Cross-Team Collaboration: Collaborate closely with internal GPU library teams to analyze and optimize distributed workloads for high throughput and low latency.Parallelization Strategies: Develop and apply optimal parallelization strategies for AI workloads to achieve best-in-class performance across diverse system configurations.Model Infrastructure and Management: Contribute to distributed model management systems, model zoos, monitoring frameworks, benchmarking pipelines, and technical documentation.Performance Monitoring and Visualization: Build and maintain real-time dashboards reporting performance, accuracy, and reliability metrics for internal stakeholders and external users.

SKILLS

Must have

• PREFERRED EXPERIENCE:AI Framework Engineering: Hands-on experience with AI inference or serving frameworks such as vLLM, SGLang, and Llama.cpp.KV Cache and Expert Parallelization: Understanding KV cache transfer mechanisms and technologies (e.g., Mooncake, NIXL/RIXL) and expert parallelization approaches (e.g., DeepEP, MORI, PPLX-Garden).Programming and Software Design: Strong C/C++ and Python skills, with experience in software design, debugging, performance analysis, and test development.Large-Scale Distributed Systems: Experience running AI workloads on large-scale, heterogeneous compute clusters.Cluster and Orchestration Systems: Familiarity with cluster management and orchestration platforms such as SLURM and Kubernetes (K8s).Development Tools and Workflows: Experience with GitHub, Jenkins, or similar CI/CD tools and modern development workflows.

Nice to have

ACADEMIC CREDENTIALS:Master’s or PhD degree in Computer Science, Computer Engineering, or a related field, or equivalent practical experience.