ML Infrastructure Engineer (AI)

TL;DR

ML Infrastructure Engineer (AI/GPU): Lead and support benchmarking of GPU platforms for machine learning and AI workloads with an accent on performance profiling, kernel-level analysis, and hardware optimization. Focus on evaluating architectures and software stacks, debugging bottlenecks, performing acceptance testing, and developing tools for visualization and data-driven decisions.

Location: Remote from Europe or United States. Applicants must be authorized to work in the country in which they apply and provide proof of employment eligibility.

Company

hirify.global is building a full-stack AI cloud platform supporting developers and enterprises from data and model training to production deployment.

What you will do

• Profile and analyze GPU performance at system and kernel level in collaboration with hardware and development teams.
• Evaluate and compare GPU performance across platforms, architectures, and software stacks like CUDA and ROCm.
• Debug and optimize ML workloads on GPU hardware, resolving performance bottlenecks.
• Conduct acceptance testing for new GPU clusters to ensure performance, stability, and compatibility for AI workloads.
• Run experiments on diverse GPU configurations to assess interconnect strategies and system optimizations.
• Develop tools and dashboards to visualize performance metrics, bottlenecks, and trends.
• Contribute to internal tooling, frameworks, and best practices.

Requirements

• Profound understanding of machine learning theoretical foundations
• Deep understanding of performance aspects of large neural networks training and inference (data/tensor parallelism, offloading, custom kernels, hardware features, attention optimizations, dynamic batching)
• Deep experience with modern deep learning frameworks (PyTorch, JAX, Megatron-LM, Tensor-LLM)
• Good understanding of GPU stack: CUDA, NCCL, drivers, relevant libraries
• Familiarity with containerized environments (Docker, Kubernetes)
• Strong communication skills and ability to work independently

Nice to have

• Familiarity with modern LLM inference frameworks (vLLM, SGLang, TensorRT)
• Experience in Python and performance profiling tools (Nsight, nvprof, perf)
• Familiarity with cloud ML platforms (AWS, GCP, Azure ML)
• Contributions to open-source ML benchmarking tools

Culture & Benefits

• Competitive compensation
• Career growth and learning opportunities
• Flexibility and work-life balance
• Collaborative and innovative culture
• Opportunity to work on impactful AI projects
• International environment with talented teams
• Fast-moving environment with bold thinking, constant growth, trust, ownership, and impact