Machine Learning Engineer (Defense)

TL;DR

Machine Learning Engineer (Defense): Build and scale multi-modal foundational models for tactical robots using self-supervised learning on Electro-Optical and Infrared data with an accent on Vision Transformer architectures and distributed multi-node GPU training. Focus on designing robust representation learning systems, optimizing training pipelines, and collaborating cross-functionally to deliver mission-critical autonomous defense AI.

Location: On-site in Paris, France

Company

hirify.global is a next-generation defense prime building autonomous and scalable defense systems, recently valued at $1.4 billion after a $200M Series B funding round.

What you will do

• Design multi-modal self-supervised learning architectures using Vision Transformers and loss functions like Masked Autoencoders and Contrastive Learning.
• Manage and optimize distributed training pipelines across multi-node GPU clusters with mixed-precision training.
• Develop evaluation metrics and benchmarks to validate learned representations before model distillation.
• Audit and enhance data lakes with cross-attention mechanisms to fuse diverse sensor data.
• Collaborate closely with Data Engineers and Edge AI teams to ensure high-performance model handoffs.

Requirements

• Must be located in Paris, France and work on-site.
• PhD or research-focused MS in Computer Science, Machine Learning, Computer Vision, or Applied Mathematics.
• 5-6+ years of experience training and scaling deep learning vision models in multi-GPU/multi-node environments.
• Proven experience with novel SSL or multi-modal architectures applied to non-standard imaging data (IR, SAR, hyperspectral).
• Strong PyTorch engineering skills and deep mathematical understanding of representation learning.
• Knowledge of system-level languages (C++, Rust, Go) and resource optimization for edge computing.
• Ability to architect fault-tolerant data pipelines and mediate hardware-algorithm trade-offs.
• Commitment to ethical defense mission and hybrid researcher-engineer mindset.