Scale AI Training Infrastructure Engineer

UMA; Paris; On-site; EUR 90,000 - 150,000; Full time

Job summary

UMA is seeking an AI Infra / Engineer for large-scale training to build scalable, reliable systems that accelerate model development. You will work with researchers to turn experimental training code into robust pipelines across thousands of GPUs, handling pretraining, mid-training and post-training on multimodal data.

The role emphasizes performance, stability and reproducibility. You will own distributed execution, optimize memory and communication, and implement efficient checkpointing and

Qualifications

• Experience building ML training infrastructure and distributed systems.
• Strong Python and PyTorch, with distributed training tools like DDP/FSDP.
• Experience with multiprocessing, asynchronous execution, and GPU memory management.
• Ability to profile workloads and translate findings into improvements.
• Collaborative mindset and clear communication with researchers.

Responsibilities

• Build distributed pretraining systems across GPUs and nodes.
• Develop RL and post-training execution infrastructure.
• Maximize MFU and eliminate bottlenecks in training loops.
• Integrate accelerated kernels and custom optimizations.
• Build training-time data movement and buffering.
• Turn research prototypes into maintainable training systems.
• Diagnose distributed and numerical failures.
• Ensure deterministic execution and reproducible runs.
• Build reliable checkpointing and fault tolerance.
• Strengthen observability and reproducibility through tracking and profiling.

Skills

Distributed training

Python

PyTorch

Profiling

Numerical debugging

Collaboration

C++/CUDA

Tools

DDP

FSDP

Megatron

Torch Distributed

Job description

UMA is seeking an AI Infra / Engineer for large-scale training to build scalable, reliable systems that accelerate model development. You will work with researchers to turn experimental training code into robust pipelines across thousands of GPUs, handling pretraining, mid-training and post-training on multimodal data.

The role emphasizes performance, stability and reproducibility. You will own distributed execution, optimize memory and communication, and implement efficient checkpointing and