ML Infrastructure Engineer (AI)

TL;DR

ML Infrastructure Engineer (AI): Building the systems behind LLM post-training, RL, evaluation, inference, and agentic development workflows with an accent on high-throughput training pipelines and data control systems. Focus on designing robust infrastructure that directly affects model learning dynamics, training stability, and product quality.

Location: Hybrid in Paris (relocation available) or London (no relocation support)

Compensation: $180,000 – $350,000 + Equity

Company

hirify.global is an AI safety company building the reliability and optimization layer for AI systems through natural-language policy enforcement and model training.

What you will do

• Build robust, flexible, and scalable RL and post-training pipelines.
• Design data control systems governing training data flow, filtering, and policy updates.
• Tune end-to-end training and inference for high throughput across networking, memory, and compute.
• Investigate how infrastructure choices impact learning dynamics, eval quality, and training stability.
• Build infrastructure for model iteration, including experiment runs, dashboards, and reproducibility.
• Develop agentic coding environments and multi-agent orchestration tools.

Requirements

• Experience designing and maintaining distributed RL or post-training systems at scale.
• Proficiency in Python, including concurrency, multiprocessing, and performance optimization.
• Familiarity with deep learning frameworks such as PyTorch or JAX.
• Ability to debug distributed GPU workloads across CUDA, networking, and storage layers.
• Experience with inference stacks like vLLM, SGLang, or TensorRT-LLM.
• Must be based in or able to work from Paris or London.

Nice to have

• Public builder footprint (open-source contributions, technical writing, or active presence in the AI infra community).
• Experience with GPU clusters on Kubernetes, Slurm, or Ray.
• Knowledge of low-level communication layers like NCCL, RDMA, or InfiniBand.
• Proficiency in Rust, C++, or Go for systems-level performance work.

Culture & Benefits

• High-impact role with significant influence on model training and safety systems.
• Opportunity to propose and run research experiments on modern ML infrastructure.
• Comprehensive medical insurance for France-based team members.
• Hardware, tools, and subscriptions for AI agents and IDEs provided.
• Team off-sites twice a year.

Hiring process

• Introductory call with HR (25 min).
• Take-home test task.
• Technical interview with Head of Applied Research (60 min).
• Final conversation with the CEO (45 min).