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 RoleAs 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.
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.
Candidate Requirements
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.
Skills Required
- PhD or highly research-focused MS in Computer Science, Machine Learning, Computer Vision, or Applied Mathematics
- 5-6+ years experience (senior-level) training and scaling deep learning vision models in multi-GPU/multi-node environments
- Proven application of SSL or multi-modal architectures (e.g., CLIP, MAE, DINO) to non-standard imaging data (IR, SAR, hyperspectral)
- Expert PyTorch engineering skills and deep mathematical understanding of representation learning
- Experience with Vision Transformers (ViTs), CNNs, masked autoencoders, contrastive learning, and cross-attention fusion techniques
- Experience managing and optimizing distributed training across multi-node GPU clusters, mixed-precision training, and data loading pipelines
- Knowledge of system-level languages (C++, Rust, or Go) and resource optimization for edge computing
- Ability to architect fault-tolerant data pipelines and mediate hardware-algorithm trade-offs across teams
- Commitment to company mission and hybrid researcher-engineer mindset valuing data quality and algorithm design
Harmattan AI Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Harmattan AI and has not been reviewed or approved by Harmattan AI.
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Fair & Transparent Compensation — Pay ranges are publicly shown for multiple U.S. roles (e.g., $140k–$200k base) and appear broadly in line with late‑stage startup/defense‑tech expectations based on the postings cited.
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Equity Value & Accessibility — Equity is repeatedly referenced in several job postings as part of total compensation, which can increase upside potential at a recently funded, high‑valuation company.
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Strong & Reliable Incentives — Sign‑on bonuses are explicitly mentioned in a hiring post for candidates who can start quickly, indicating the use of cash incentives in at least some hiring situations.
Harmattan AI Insights
What We Do
Harmattan AI is rising as a next-generation defense prime, building the future of autonomous warfare. We leverage AI-driven autonomy, real-time intelligence, and conflict-ready production to deliver attritable systems and autonomous mission management software. Designed for the real-world needs of warfighters, our solutions enable faster deployment, sharper decision-making, and battlefield dominance.
