Machine Learning Engineer - Foundational
Harmattan AI3 months ago
Zürich, SwitzerlandSenior
Responsibilities
- Design multimodal self-supervised learning architectures and loss functions for paired and unpaired EO and IR data.
- Build and optimize distributed training pipelines across multi-node GPU clusters, including mixed-precision training and data loading.
- Develop representation-quality metrics and linear-probing benchmarks before model distillation.
- Audit EO/IR data lakes and implement cross-attention mechanisms for diverse sensor features.
- Collaborate with Data Engineers on ingestion pipelines and with the Edge AI team on high-performance model handoffs.
- Architect fault-tolerant data-pipeline state machines and mediate hardware-versus-algorithm trade-offs.
Requirements
- PhD or highly research-focused MS in Computer Science, Machine Learning, Computer Vision, or Applied Mathematics.
- Minimum 5–6 years of experience for senior levels.
- Experience training and scaling deep-learning vision models such as ViTs and CNNs from scratch in multi-GPU and multi-node environments.
- Experience applying novel self-supervised or multimodal architectures such as CLIP, MAE, or DINO to real-world non-standard imaging data including IR, SAR, or hyperspectral data.
- Strong PyTorch engineering skills and deep mathematical intuition for representation learning.
- Knowledge of C++, Rust, or Go and resource optimization for edge computing.
- Ability to bridge research and engineering concerns and make technical trade-offs across hardware and algorithm teams.
- Commitment to Harmattan AI’s mission of providing an ethical defense edge to allied countries.
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