Lead GPU ML Infrastructure & Acceleration Engineer

Prosus; Amsterdam; Hybrid; EUR 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid work model

Amsterdam office

Job summary

Prosus is seeking an experienced ML Infrastructure & Acceleration Engineer to own the GPU infrastructure powering our LLM training and serving. You will run the H200 cluster end to end, deploy models to production, profile and optimize pipelines, and work with ML teams to turn raw compute into faster iteration for millions of users.

You will operate multi-node GPU clusters with Kubernetes and Slurm, monitor performance, and push for efficiency and reliability.

Qualifications

• 5+ years in ML infrastructure, MLOps, HPC, or platform engineering with focus on GPU workloads.
• Hands-on experience operating multi-node GPU clusters with Kubernetes and/or Slurm, including debugging hardware, networking (NCCL, InfiniBand/RoCE), and job failures.
• Production experience deploying and optimizing LLM inference with vLLM, SGLang, or TensorRT-LLM.
• Proven track record of profiling and accelerating GPU workloads, with measured results in throughput, latency, MFU, or cost.
• Solid understanding of monitoring and observability for GPU systems (Prometheus, Grafana, DCGM, or similar).
• Naturally curious with a track record of proactively identifying and implementing improvements.

Responsibilities

• Own our H200 GPU cluster end to end: Kubernetes and Slurm for training and inference workloads, with multi-tenant scheduling, quotas, and resource isolation
• Keep large distributed training runs healthy: node health checks, NCCL and interconnect debugging, automated job recovery, and checkpoint management
• Track and optimize GPU utilization and cost across training and inference, with dashboards and budget alerting
• Manage infrastructure-as-code and provisioning for GPU environments
• Define and monitor SLIs/SLOs for cluster availability and production model serving
• Deploy LLMs to production using vLLM or SGLang, with CI/CD, model versioning, and safe rollouts and rollbacks
• Monitor serving latency, throughput, errors, and cost, and act on regressions
• Profile training and inference end to end (PyTorch Profiler, Nsight Systems, Nsight Compute) to find and remove bottlenecks
• Improve training throughput and MFU: parallelism strategies (FSDP, tensor and pipeline parallelism), communication/compute overlap, mixed precision and FP8, activation checkpointing, torch.compile
• Optimize inference: quantization (FP8, INT8, INT4), speculative decoding, prefix caching, continuous batching, KV cache tuning, and parallelism layouts
• Write or integrate fused and custom kernels (Triton, CUDA) where off-the-shelf implementations fall short
• Build benchmarking tooling so every optimization ships with measured gains
• Partner with ML engineers and researchers to make training and serving efficient by default
• Establish best practices for using the cluster and guide teams in their adoption

Skills

ML infrastructure

Naturally curious

Profiling & acceleration

Tools

Kubernetes

Slurm

NCCL

InfiniBand/RoCE

TensorRT-LLM

vLLM

SGLang

PyTorch Profiler

Nsight Systems

Nsight Compute

Triton

CUDA

Job description

Prosus is seeking an experienced ML Infrastructure & Acceleration Engineer to own the GPU infrastructure powering our LLM training and serving. You will run the H200 cluster end to end, deploy models to production, profile and optimize pipelines, and work with ML teams to turn raw compute into faster iteration for millions of users.

You will operate multi-node GPU clusters with Kubernetes and Slurm, monitor performance, and push for efficiency and reliability.