Machine Learning Engineer - Foundational

Harmattan AI; Schweiz; On-site; CHF 150,000 - 190,000; Full time

Job summary

Harmattan AI seeks a senior ML Engineer to design multi-modal SSL architectures and scale models on large EO/IR data. You will lead distributed training across multi-GPU clusters and collaborate with data engineers to ensure robust data ingestion and high-performance handoffs to Edge AI teams.

The role requires a PhD or a highly research-focused MS in CS/ML/CV, 5–6+ years of relevant experience, and expertise in PyTorch, with strong mathematical intuition and systems programming in C++, Rust, or

Qualifications

• PhD or highly research-focused MS in Computer Science, Machine Learning, Computer Vision, or Applied Mathematics.
• 5–6+ years of experience for senior levels and experience training and scaling deep learning vision models (ViTs, CNNs) in multi-GPU/multi-node environments.
• Proven experience applying novel SSL or multi-modal architectures (e.g., CLIP, MAE, DINO) to real-world imaging data (EO/IR/SAR/hyperspectral) and strong PyTorch engineering skills.

Responsibilities

• Design neural architectures for multi-modal SSL on paired and unpaired EO/IR data.
• Manage and optimize distributed training pipelines across multi-node GPU clusters.
• Develop metrics and benchmarks to evaluate latent representations before distillation.
• Audit EO/IR data lakes and implement cross-attention to fuse sensor features.
• Collaborate with Data Engineers on ingestion and with Edge AI for model handoffs.

Skills

PyTorch engineering

Mathematical intuition

Leadership

Education

PhD in CS/ML/CV/Applied Mathematics

MS in CS/ML/CV/Applied Mathematics

Tools

PyTorch

C++

Rust

Go

Job description

About Us

Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission‑critical systems to allied forces.

Our work is guided by clear values: building technologies with real‑world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected.

About the Role

As a Machine Learning Engineer on our Foundational team in Paris, you will build the “brain” of our tactical robots. You will design and scale large-scale, multi‑modal foundational models that learn robust representations of the battlefield using Self‑Supervised Learning (SSL) from massive amounts of unlabelled Electro‑Optical (EO) and Infrared (IR) data. Your work provides the critical foundational weights that our Edge AI team distills into hyper‑accurate models running on tactical hardware.

Responsibilities

• Multi‑Modal SSL Architecture Design: Design neural network architectures (Vision Transformers) and loss functions (Masked Autoencoders, Contrastive Learning) to jointly learn from paired and unpaired EO and IR data.

• Distributed Training Infrastructure: Manage and optimise training pipelines across multi‑node GPU clusters, handling mixed‑precision training and data loading.

• Representation Evaluation: Develop metrics and linear‑probing benchmarks to prove the latent space captures useful semantic features before distillation.

• Data Strategy: Audit existing EO/IR data lakes and implement cross‑attention mechanisms to fuse diverse sensor features.

• Cross‑Functional Collaboration: Sync with Data Engineers on ingestion pipelines and collaborate with the Edge AI team to ensure high‑performance model handoffs.

• Educational Background: A PhD or a highly research‑focused MS in Computer Science, Machine Learning, Computer Vision, or Applied Mathematics.

• Proven Experience: Minimum of 5‑6 years of experience for senior levels. Experience training and scaling deep learning vision models (ViTs, CNNs) from scratch in multi‑GPU/multi‑node environments. Successful application of novel SSL or multi‑modal architectures (e.g., CLIP, MAE, DINO) to real‑world, non‑standard imaging data (IR, SAR, or hyperspectral).

• Technical Proficiency: Hardcore PyTorch engineering skills combined with deep mathematical intuition for representation learning. Knowledge of system‑level languages (C++, Rust, or Go) and resource optimisation for edge computing.

• Complexity & Leadership: Ability to architect state machines for fault‑tolerant data pipelines and mediate technical trade‑offs between hardware and algorithm teams.

• Commitment & Mindset: 100% dedication to Harmattan AI’s mission of providing an ethical defence edge to allied countries. A hybrid researcher‑engineer mindset that treats data quality as seriously as algorithm design

We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI.