Machine Learning Engineer - Foundational

Harmattan AI; Paris; Sur place; EUR 70 000 - 90 000; Plein temps

Résumé du poste

A defense technology company is seeking a Machine Learning Engineer in Paris to design and scale multi-modal foundational models that learn robust battlefield representations. Candidates should have a PhD and 5-6 years of experience in deep learning and PyTorch. The role involves managing training infrastructures and collaborating cross-functionally to ensure high-performance model handoffs. Excellent problem-solving and leadership skills are essential, along with a commitment to the ethical development of defense technologies.

Qualifications

• Minimum of 5-6 years of experience for senior levels.
• Experience training and scaling deep learning vision models from scratch.
• Successful application of novel SSL or multi-modal architectures.

Responsabilités

• Design neural network architectures and loss functions for multi-modal SSL.
• Manage training pipelines across multi-node GPU clusters.
• Develop metrics to evaluate latent space for semantic features.

Connaissances

PyTorch engineering

Deep learning vision models

System-level languages (C++, Rust, Go)

Data quality management

Formation

PhD or highly research-focused MS in Computer Science, Machine Learning, Computer Vision, or Applied Mathematics

Outils

Multi-GPU/multi-node environments

Description du poste

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.

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.