Machine Learning Engineer - Foundational
Zurich · Zurich, Switzerland
- Pay
- Pay not listed
- Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
R&D — team source
Department: R&D
From the employer’s posting
What you’ll work on
Full postingAs a Machine Learning Engineer on our Foundational team in Paris, you will build the "brain" of our tactical robots.
Design Vision Transformer architectures and SSL loss functions for EO and IR data
Manage distributed training pipelines across multi-node GPU clusters
Build metrics and linear-probing benchmarks to test latent space quality
Audit EO/IR data lakes and implement cross-attention for sensor fusion
Coordinate ingestion pipelines with Data Engineers and model handoffs with Edge AI
From the employer’s posting
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.
Design neural network architectures (Vision Transformers) and loss functions (Masked Autoencoders, Contrastive Learning) to jointly learn from paired and unpaired EO and IR data.
Manage and optimise training pipelines across multi-node GPU clusters, handling mixed-precision training and data loading.
Develop metrics and linear-probing benchmarks to prove the latent space captures useful semantic features before distillation.
Audit existing EO/IR data lakes and implement cross-attention mechanisms to fuse diverse sensor features.
Sync with Data Engineers on ingestion pipelines and collaborate with the Edge AI team to ensure high-performance model handoffs.
Education & alternatives
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).
Tools in this posting
- C++
- Rust
- PyTorch
- Go
Source — Tool mentions in context
- 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.
About Harmattan AI
Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems.
In the employer’s words · Read in context · Company website ↗
Posting record
Report an errorOpen 4 monthsWe first saw this posting 135 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 18, 2026 · 135 days ago
- Recorded sightings
- 288
- Last seen by us
- Nov 1, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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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.
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.
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Zurich, Switzerland
- 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.
- Work authorization
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