ML Infrastructure Engineer

Jobgether • Germany

Remote

This Job is No Longer Active This position is no longer accepting applications

AI Summary

Key Highlights

Benchmark and evaluate GPU platform performance

Collaborate with hardware and engineering teams

Analyze, debug, and optimize training and inference workloads

Key Responsibilities

Benchmark and evaluate GPU platform performance

Collaborate with hardware and engineering teams

Analyze, debug, and optimize training and inference workloads

Conduct acceptance testing for new GPU clusters

Develop internal tools, dashboards, and reporting frameworks

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Technical Skills Required

PyTorch JAX Megatron-LM TensorRT-LLM CUDA NCCL GPU drivers performance-related libraries NVIDIA Nsight nvprof perf Docker Kubernetes Python

Benefits & Perks

Competitive compensation package

Flexible remote work environment

Access to continuous learning, career development, and growth opportunities

Opportunity to work on impactful AI projects

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Nice to Have

Experience with LLM inference frameworks

Familiarity with cloud-based ML ecosystems

Contributions to open-source ML tooling, benchmarking frameworks, or infrastructure projects

Job Description

Join a cutting-edge AI infrastructure environment focused on powering the next generation of machine learning and large-scale AI workloads. This role offers the opportunity to work at the intersection of GPU performance engineering, deep learning optimization, and cloud-scale infrastructure development. You will contribute directly to benchmarking and optimizing advanced GPU platforms that support training and inference for complex neural networks and AI systems. Working alongside highly skilled engineering and hardware teams, you will help drive performance improvements across compute architectures, software stacks, and distributed AI environments. The position is ideal for engineers passionate about ML systems, large-scale model performance, and infrastructure innovation. With exposure to modern AI frameworks, high-performance GPU ecosystems, and international collaboration, this role provides a strong platform for technical growth and meaningful impact within the AI industry.

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