ML Infrastructure Engineer
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Salary analysis
Compared with the selected benchmark ("Company in Remote - Europe"), this listing's salary midpoint is about 89% lower. The offer sits below the benchmark range (€1,667–€6,000). The listed pay band (€319–€500) is tighter than the benchmark, which suggests lower salary variability. This benchmark is based on 6 comparable listings.
| Market | Lower bound (25th percentile) | Median | Upper bound (75th percentile) |
|---|---|---|---|
| All roles in Remote - Europe | €1,978/per month | €4,914/per month | €13,713/per month |
| Company in Remote - Europe | €1,667/per month | €2,056/per month | €6,000/per month |
| Pay in our data — not quoted in ad (Senior) | €319/per month | €410/per month | €500/per month |
The role involves leading and supporting benchmarking of GPU platforms for machine learning and AI workloads. You will evaluate performance across CUDA, ROCm and various stacks, profile GPU performance, debug and optimize ML workloads, perform acceptance testing for new GPU clusters, and develop tools and dashboards to visualize metrics. You will work with hardware, development teams and contribute to internal tooling, frameworks and best practices. Requirements include deep understanding of ML fundamentals, experience with modern DL frameworks (PyTorch, JAX, Megatron-LM, Tensor-LLM) and familiarity with containerized environments (Docker, Kubernetes). Strong communication and independence are essential.
Job Details
Responsibilities
- Profile and analyze GPU performance at system and kernel level
- Evaluate performance across platforms and software stacks
- Debug and optimize ML workloads for GPU hardware
- Perform acceptance testing for new GPU clusters
- Develop tools and dashboards to visualize performance metrics
- Contribute to internal tooling and best practices
Requirements
- Deep understanding of machine learning fundamentals
- Experience with PyTorch, JAX, Megatron-LM, Tensor-LLM
- Familiarity with CUDA, NCCL, drivers and libraries
- Containerized environments (Docker, Kubernetes)
- Strong communication and independence
Skills & Technologies
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