AI Scientist / ML Engineer

IntFRTODWHS

Apply for this position and join us in building Europe’s next generation infrastructure.

Our values are rooted in responsibility, readiness, and long term thinking. We believe sovereignty must be designed into systems from the start. Readiness is achieved through capability, not procurement. Autonomy is infrastructure, not a feature. We build with the understanding that modern systems carry long term consequences. That is why we prioritise reliability over novelty, integration over isolation, and sustained capability over short term advantage.

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Position

Role Overview

Eligibility: Candidates must be nationals of an EU member state, NATO member state, or NATO-allied

partner nation. Required due to defense/dual-use classification of projects (EU Dual-Use Regulation

2021/821, ITAR).

Own LUXUAV's sovereign AI capability: conduct original ML research, train and optimize proprietary perception

models on classified aerial datasets, and build the end-to-end infrastructure that productionises them. This role is

split between applied research (novel model design, domain-specific learning methods, adversarial robustness) and

MLOps (training pipelines, model CI/CD, edge deployment automation). All training and inference must remain fully

on-premise — no third-party vision APIs or cloud inference are permitted.

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Key Responsibilities

AI Research & Model Development

• Conduct original research into perception architectures tailored to aerial defense use cases: small object
  
  

detection at variable altitude, multi-class threat classification, camouflage/occlusion robustness, and EO/IR

domain adaptation.

• Design, train, and evaluate real-time detection models (YOLOv8+, RT-DETR, custom architectures) and
  
  

propose novel adaptations where COTS solutions fall short of operational requirements.

• Research and implement domain adaptation and transfer learning strategies to maximize model performance
  
  

on limited, classified aerial datasets without external data augmentation from third-party sources.

• Develop active learning and semi-supervised learning pipelines to minimize annotation cost on high-security
  
  

datasets.

• Investigate and prototype multi-task learning approaches combining detection, segmentation, and scene
  
  

understanding in a single inference pass for onboard efficiency.

• Develop and maintain multi-target tracking modules (ByteTrack, StrongSORT, or custom) as standalone,
  
  

testable components delivered to the CV Engineer for pipeline integration.

• Maintain awareness of state-of-the-art ML research (NeurIPS, CVPR, ICCV, ECCV, ICML) and evaluate
  
  

applicability to LUXUAV's operational context.

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Edge Model Optimisation & Deployment Packaging

• Optimize trained models for onboard GPU hardware (NVIDIA Jetson, Qualcomm RB5) via quantization
  
  

(INT8/FP16), structured pruning, and knowledge distillation.

• Export and validate optimized models through TensorRT, ONNX Runtime, and OpenVINO toolchains; produce
  
  

deployment packages with performance benchmarks (latency, throughput, memory footprint) for handoff to the

CV Engineer.

• Define and maintain model performance contracts (accuracy floors, latency ceilings) gating production
  
  

promotion.

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ML Infrastructure & MLOps

• Design and maintain GPU training pipelines (multi-GPU, distributed training) on LUXUAV's on-premise GPU
  
  

cluster.

• Own experiment tracking, model registry, and reproducibility tooling (MLflow, DVC, or equivalent); enforce
  
  

dataset and model versioning across all AI projects.

• Build and operate CI/CD pipelines for model releases: automated regression testing, benchmark gating,
  
  

staged rollout, and rollback procedures.

• Manage the on-premise multi-GPU server cluster: resource scheduling, utilization monitoring,
  
  

driver/framework version management, and capacity planning.

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Synthetic Data & Digital Twin Integration

• Design and operate synthetic data generation pipelines (scene rendering, domain randomization,
  
  

augmentation) to supplement classified real-world datasets, feeding LUXUAV's Digital Twin program.

• Maintain dataset quality pipelines: annotation validation, class balance monitoring, and distribution drift
  
  

detection between training and operational data.

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Security & Data Governance

• Enforce air-gapped data handling protocols for all classified training datasets: access control, lineage tracking,
  
  

and audit logging.

• Ensure all ML tooling, model artifacts, and training infrastructure comply with data classification policies and
  
  

export control requirements.

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Experience & Skills

Required Qualifications & Experience:

• Master's or PhD in Machine Learning, Computer Vision, Applied Mathematics, or related field — PhD strongly
  
  

preferred for the research component.

• 5+ years of hands-on experience in ML research and model development for real-world vision tasks
  
  

(detection, classification, segmentation, tracking).

• Demonstrated ability to go from research idea to working implementation: paper reproduction, ablation
  
  

studies, and production-grade code.

• Strong proficiency in PyTorch; familiarity with TensorFlow/Keras and ONNX.
• Proven experience with detection/segmentation frameworks (YOLOv8+, RT-DETR, Detectron2,
  
  

MMDetection).

• Practical expertise in model optimization for edge: quantization, pruning, distillation, TensorRT/ONNX Runtime
  
  

export.

• Experience with domain adaptation, transfer learning, and data-efficient learning techniques.
• Hands-on experience building MLOps pipelines: experiment tracking (MLflow, Neptune.ai, W&B), dataset
  
  

versioning (DVC), and model CI/CD.

• Proficiency in Python (NumPy, SciPy, scikit-learn, Pandas) and pipeline automation scripting.
• Experience managing or operating multi-GPU Linux training environments (CUDA, cuDNN, SLURM or
  
  

equivalent).

• English: Upper Intermediate or higher.
• Good communication skills and ability to cooperate with adjacent engineering teams.
• National from a NATO member country or one of the following NATO Indo-Pacific partners: Australia, Japan,
  
  

South Korea, New Zealand or Ukraine.

• Free criminal record
  
  

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Preferred Qualifications & Experience

• Track record of publication at NeurIPS, CVPR, ICCV, ECCV, ICML, IROS, or equivalent venues; open-source
  
  

contributions to ML or CV frameworks.

• Experience with active learning, semi-supervised learning, or self-supervised pretraining on domain-specific
  
  

datasets.

• Familiarity with aerial imagery specifics: overhead perspective, small object detection, multi-scale challenges,
  
  

EO/IR domain adaptation.

• Experience with synthetic data generation and domain randomization (Blender, NVIDIA Isaac Sim, Unreal
  
  

Engine/AirSim, or equivalent).

• Experience with adversarial robustness evaluation and model hardening for safety-critical deployments.
• Experience with distributed training frameworks (PyTorch DDP, DeepSpeed, FSDP).
• Familiarity with on-premise MLOps stacks (self-hosted MLflow, MinIO, Prometheus/Grafana for GPU
  
  

monitoring).

• Familiarity with DO-178C and MISRA C/C++ as they apply to ML model validation in airborne systems.
  
  

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